7663509
Recurrent drifts: Aapplying fuzzy logic to concept similarity function
Recurrent drift, as a specific type of concept drift, is characterised by the appearance of previously seen concepts. Therefore, in those cases the learning process could be saved or at least minimized by applying an already trained classification model. In this paper we propose Fuzzy-Rec, a framework that is able to deal with recurrent concept drifts by means of a repository of classification models and a similarity function. Fuzzy logic is used in the framework to implement the similarity function needed to compare different classification models. This is a crucial aspect when dealing with drift recurrence, as long as some measure must be implemented to determine which model better fits a previously seen context. As it can be seen in the experimentation results of this paper, this fuzzy similarity function provides excellent results both in synthetic and real datasets. As a conclusion, we can state that the introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, saving computational resources by applying not just equal models, but also similar ones.
2606652
Learning with Drift Detection
Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generate the examples changes over time. We present a method for detection of changes in the probability distribution of examples. The idea behind the drift detection method is to control the online error-rate of the algorithm. The training examples are presented in sequence. When a new training example is available, it is classified using the actual model. Statistical theory guarantees that while the distribution is stationary, the error will decrease. When the distribution changes, the error will increase. The method controls the trace of the online error of the algorithm. For the actual context we define a warning level, and a drift level. A new context is declared, if in a sequence of examples, the error increases reaching the warning level at example k w , and the drift level at example k d . This is an indication of a change in the distribution of the examples. The algorithm learns a new model using only the examples since k w . The method was tested with a set of eight artificial datasets and a real world dataset. We used three learning algorithms: a perceptron, a neural network and a decision tree. The experimental results show a good performance detecting drift and with learning the new concept. We also observe that the method is independent of the learning algorithm.
15558554
Learning in the Presence of Concept Drift and Hidden Contexts
On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and re-using them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift.
60834590
Knowledge discovery from data streams
Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.
14584150
Tracking Recurring Concepts with Meta-learners
This work address data stream mining from dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current model, and start learning a new decision model, Nevertheless, it is not rare for the concepts from history to reappear, for example seasonal changes. In this work we present method that memorizes learnt decision models whenever a concept drift is signaled. The system uses meta-learning techniques that characterize the domain of applicability of previous learnt models. The meta-learner can detect re-occurrence of contexts and take pro-active actions by activating previous learnt models. The main benefit of this approach is that the proposed meta-learner is capable of selecting similar historical concept, if there is one, without the knowledge of true classes of examples.
19277476
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Concept drift constitutes a challenging problem for the machine learning and data mining community that frequently appears in real world stream classification problems. It is usually defined as the unforeseeable concept change of the target variable in a prediction task. In this paper, we focus on the problem of recurring contexts, a special sub-type of concept drift, that has not yet met the proper attention from the research community. In the case of recurring contexts, concepts may re-appear in future and thus older classification models might be beneficial for future classifications. We propose a general framework for classifying data streams by exploiting stream clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual representation model is proposed. The clustering algorithm is then applied in order to group batches of examples into concepts and identify recurring contexts. The ensemble is produced by creating and maintaining an incremental classifier for every concept discovered in the data stream. An experimental study is performed using (a) two new real-world concept drifting datasets from the email domain, (b) an instantiation of the proposed framework and (c) five methods for dealing with drifting concepts. Results indicate the effectiveness of the proposed representation and the suitability of the concept-specific classifiers for problems with recurring contexts.
11534248
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Prediction in streaming data is an important activity in the modern society. Two major challenges posed by data streams are (1) the data may grow without limit so that it is difficult to retain a long history of raw data; and (2) the underlying concept of the data may change over time. The novelties of this paper are in four folds. First, it uses a measure of conceptual equivalence to organize the data history into a history of concepts. This contrasts to the common practice that only keeps recent raw data. The concept history is compact while still retains essential information for learning. Second, it learns concept-transition patterns from the concept history and anticipates what the concept will be in the case of a concept change. It then proactively prepares a prediction model for the future change. This contrasts to the conventional methodology that passively waits until the change happens. Third, it incorporates proactive and reactive predictions. If the anticipation turns out to be correct, a proper prediction model can be launched instantly upon the concept change. If not, it promptly resorts to a reactive mode: adapting a prediction model to the new data. Finally, an efficient and effective system RePro is proposed to implement these new ideas. It carries out prediction at two levels, a general level of predicting each oncoming concept and a specific level of predicting each instance's class. Experiments are conducted to compare RePro with representative existing prediction methods on various benchmark data sets that represent diversified scenarios of concept change. Empirical evidence offers inspiring insights and demonstrates the proposed methodology is an advisable solution to prediction in data streams.
16564127
Calculus with Fuzzy Numbers
Algebra of ordered fuzzy numbers (OFN) is defined to handle with fuzzy inputs in a quantitative way, exactly in the same way as with real numbers. Additional two structures: algebraic and normed (topological) are introduced to define a general form of defuzzyfication operators. A useful implementation of a Fuzzy Calculator allows counting with the general type membership relations.
5705465
Next challenges for adaptive learning systems
Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.
11868540
A streaming ensemble algorithm (SEA) for large-scale classification
Ensemble methods have recently garnered a great deal of attention in the machine learning community. Techniques such as Boosting and Bagging have proven to be highly effective but require repeated resampling of the training data, making them inappropriate in a data mining context. The methods presented in this paper take advantage of plentiful data, building separate classifiers on sequential chunks of training points. These classifiers are combined into a fixed-size ensemble using a heuristic replacement strategy. The result is a fast algorithm for large-scale or streaming data that classifies as well as a single decision tree built on all the data, requires approximately constant memory, and adjusts quickly to concept drift.
14379357
Tracking recurrent concept drift in streaming data using ensemble classifiers
Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. We present an ensemble learning based approach to handle the data streams having multiple underlying modes. We build a global set of classifiers from sequential data chunks; ensembles are then selected from this global set of classifiers, and new classifiers created if needed, to represent the current concept in the stream. The system is capable of performing any-time classification and to detect concept drift in the stream. In streaming data historic concepts are likely to reappear so we don't delete any of the historic classifiers. Instead, we judiciously select only pertinent classifiers from the global set while forming the ensemble set for a classification task.
16726554
Tracking Recurrent Concepts Using Context
The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several methods have been proposed that are able to learn in the presence of concept drift, few consider concept recurrence and integration of context. In this work, we extend existing drift detection methods to deal with this problem by exploiting context information associated with learned decision models in situations where concepts reappear. The preliminary experimental results demonstrate the effectiveness of the proposed approach for data stream classification problems with recurring concepts.
16659360
Accuracy Updated Ensemble for Data Streams with Concept Drift
In this paper we study the problem of constructing accurate block-based ensemble classifiers from time evolving data streams. AWE is the best-known representative of these ensembles. We propose a new algorithm called Accuracy Updated Ensemble (AUE), which extends AWE by using online component classifiers and updating them according to the current distribution. Additional modifications of weighting functions solve problems with undesired classifier excluding seen in AWE. Experiments with several evolving data sets show that, while still requiring constant processing time and memory, AUE is more accurate than AWE.
6786471
Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges in learning from data streams is reacting to concept drift, i.e., unforeseen changes of the stream's underlying data distribution. Several classification algorithms that cope with concept drift have been put forward, however, most of them specialize in one type of change. In this paper, we propose a new data stream classifier, called the Accuracy Updated Ensemble (AUE2), which aims at reacting equally well to different types of drift. AUE2 combines accuracy-based weighting mechanisms known from block-based ensembles with the incremental nature of Hoeffding Trees. The proposed algorithm is experimentally compared with 11 state-of-the-art stream methods, including single classifiers, block-based and online ensembles, and hybrid approaches in different drift scenarios. Out of all the compared algorithms, AUE2 provided best average classification accuracy while proving to be less memory consuming than other ensemble approaches. Experimental results show that AUE2 can be considered suitable for scenarios, involving many types of drift as well as static environments.
9136731
Incremental Learning of Concept Drift in Nonstationary Environments
We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn++.NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn++ family of algorithms, that is, without requiring access to previously seen data. Learn++.NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn++.NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper.
10725353
Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes
We have previously introduced an incremental learning algorithm Learn++, which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn++ suffers from an inherent ldquooutvotingrdquo problem when asked to learn a new class omeganew introduced by a subsequent data set, as earlier classifiers not trained on this class are guaranteed to misclassify omeganew instances. The collective votes of earlier classifiers, for an inevitably incorrect decision, then outweigh the votes of the new classifiers' correct decision on omeganew instances-until there are enough new classifiers to counteract the unfair outvoting. This forces Learn++ to generate an unnecessarily large number of classifiers. This paper describes Learn++ .NC, specifically designed for efficient incremental learning of multiple new classes using significantly fewer classifiers. To do so, Learn ++.NC introduces dynamically weighted consult and vote (DW-CAV) , a novel voting mechanism for combining classifiers: individual classifiers consult with each other to determine which ones are most qualified to classify a given instance, and decide how much weight, if any, each classifier's decision should carry. Experiments on real-world problems indicate that the new algorithm performs remarkably well with substantially fewer classifiers, not only as compared to its predecessor Learn++, but also as compared to several other algorithms recently proposed for similar problems.
1111538
New Management Operations on Classifiers Pool to Track Recurring Concepts
Handling recurring concepts has become of interest as a challenging problem in the field of data stream classification in recent years. One main feature of data streams is that they appear in nonstationary environments. This means that the concept which the data are drawn from, changes over the time. If after a long enough time, the concept reverts to one of the previous concepts, it is said that recurring concepts has occurred. One solution to this challenge is to maintain a pool of classifiers, each representing a concept in the stream. This paper follows this approach and holds an ensemble of classifiers for each concept. As for each received batch of data, a new classifier is created; there will be a huge amount of classifiers which could not be maintained in the pool. To handle the memory limitations, a maximum number of concepts and classifiers are assumed. So the necessity of managing the concepts and classifiers is obvious. This paper presents a novel algorithm to manage the pool. Some pool management operations including merging and splitting the concepts are introduced. Experimental results show the performance dominance of using our method to the most promising stream classification algorithms.
14823702
Exponentially weighted moving average charts for detecting concept drift
Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an exponentially weighted moving average (EWMA) chart to monitor the misclassification rate of an streaming classifier. Our approach is modular and can hence be run in parallel with any underlying classifier to provide an additional layer of concept drift detection. Moreover our method is computationally efficient with overhead O(1) and works in a fully online manner with no need to store data points in memory. Unlike many existing approaches to concept drift detection, our method allows the rate of false positive detections to be controlled and kept constant over time.
44878371
RCD: A recurring concept drift framework
This paper presents recurring concept drifts (RCD), a framework that offers an alternative approach to handle data streams that suffer from recurring concept drifts (on-line learning). It creates a new classifier to each context found and stores a sample of data used to build it. When a new concept drift occurs, the algorithm compares the new context to previous ones using a non-parametric multivariate statistical test to verify if both contexts come from the same distribution. If so, the corresponding classifier is reused. The RCD framework is compared with several algorithms (among single and ensemble approaches), in both artificial and real data sets, chosen from frequently used algorithms and data sets in the concept drift research area. We claim the proposed framework had better average ranks in data sets with abrupt and gradual concept drifts compared to both the single classifiers and the ensemble approaches that use the same base learner.
11443945
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
Tracking recurring concept drifts is a significant issue for machine learning and data mining that frequently appears in real-world stream classification problems. It is a challenge for many streaming classification algorithms to learn recurring concepts in a data stream environment with unlabeled data, and this challenge has received little attention from the research community. Motivated by this challenge, this article focuses on the problem of recurring contexts in streaming environments with limited labeled data. We propose a semi-supervised classification algorithm for data streams with REcurring concept Drifts and Limited LAbeled data, called REDLLA, in which a decision tree is adopted as the classification model. When growing a tree, a clustering algorithm based on k-means is installed to produce concept clusters and unlabeled data are labeled in the method of majority-class at leaves. In view of deviations between history and new concept clusters, potential concept drifts are distinguished and recurring concepts are maintained. Extensive studies on both synthetic and real-world data confirm the advantages of our REDLLA algorithm over three state-of-the-art online classification algorithms of CVFDT, DWCDS, and CDRDT and several known online semi-supervised algorithms, even in the case with more than 90% unlabeled data.
14348632
Learning recurring concepts from data streams with a context-aware ensemble
The dynamic and unstable nature observed in real world applications influences learning systems through changes in data, context and resource availability. Data stream mining systems must be aware and adapt to such changes so that incoming data can continuously be classified with high accuracy. Ensemble approaches have been shown successful in dealing with concept changes. Despite their success in learning under concept changes, context information has not yet been exploited by ensemble approaches in data stream scenarios where concepts reappear. Under these circumstances, context information appropriately integrated with learned concepts would enable to anticipate recurring changes in concepts. In this work, we present an ensemble based approach for the problem of detecting concept changes in data streams where concepts reappear, that dynamically adds and removes weighted classifiers in response to changes not only in concepts but to context. We identify stable concepts using a change detection method, based on the error-rate of the learning process. Context information is used in the adaptation to recurring concepts and in the management of knowledge from previous learned concepts while adapting to resource constraints. Consequently, proper representation and storage of context and concepts is a major issue dealt within the paper. We present and discuss preliminary experimental results with synthetic and real datasets.
52800392
Detecting Change in Data Streams
Detecting changes in a data stream is an important area of research with many applications. In this paper, we present a novel method for the detection and estimation of change. In addition to providing statistical guarantees on the reliability of detected changes, our method also provides meaningful descriptions and quantification of these changes. Our approach assumes that the points in the stream are independently generated, but otherwise makes no assumptions on the nature of the generating distribution. Thus our techniques work for both continuous and discrete data. In an experimental study we demonstrate the power of our techniques.
10110725
EVE: a framework for event detection
In this paper, we introduce EVE, a generic framework for event detection where events can also include outliers, model changes and drifts. Various methods for event detection have been proposed for different types of events. However, many of them make the same or very similar prior assumptions but use different notations and formalizations. EVE provides a general framework for event detection, which allows existing algorithms to be represented using a common basis. The framework includes generic types of time slots, streaming progresses, and measures of similarity between those slots. We demonstrate how existing algorithms fit nicely into this framework by instantiating appropriate window combinations, progress mechanisms, and similarity functions.
23207009
Detecting Concept Drift Using Statistical Testing
Detecting concept drift is important for dealing with realworld online learning problems. To detect concept drift in a small number of examples, methods that have an online classifier and monitor its prediction errors during the learning have been developed. We have developed such a detection method that uses a statistical test of equal proportions. Experimental results showed that our method performed well in detecting the concept drift in five synthetic datasets that contained various types of concept drift.
15672006
Early Drift Detection Method
An emerging problem in Data Streams is the detection of concept drift. This problem is aggravated when the drift is gradual over time. In this work we deflne a method for detecting concept drift, even in the case of slow gradual change. It is based on the estimated distribution of the distances between classiflcation errors. The proposed method can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental and online algorithm. The experimentation results compare our method (EDDM) with a similar one (DDM). Latter uses the error-rate instead of distance-error-rate.
12397401
MOA: Massive Online Analysis
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.
2376050
jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation
This work introduces jFuzzyLogic, an open source library for fuzzy systems which allow us to design Fuzzy Logic Controllers supporting the standard for Fuzzy Control Programming published by the International Electrotechnical Commission. This library is written in Java and is available as open source from jfuzzylogic.sourceforge.net. The use of jFuzzyLogic is illustrated through the analysis of one case study.
667586
Estimating Continuous Distributions in Bayesian Classifiers
When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.
147194189
Nonparametric Statistical Methods
This Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its predecessor, the revised edition, along with its companion ftp site, aims to equip readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics.