1977839
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single “best” classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence.
9199797
Error Detection and Impact-Sensitive Instance Ranking in Noisy Datasets
Given a noisy dataset, how to locate erroneous instances and attributes and rank suspicious instances based on their impacts on the system performance is an interesting and important research issue. We provide in this paper an Error Detection and Impact-sensitive instance Ranking (EDIR) mechanism to address this problem. Given a noisy dataset D, we first train a benchmark classifier T from D. The instances, that cannot be effectively classified by T are treated as suspicious and forwarded to a subset S. For each attribute Ai, we switch Ai and the class label C to train a classifier APi for Ai. Given an instance Ik in S, we use APi and the benchmark classifier T to locate the erroneous value of each attribute Ai. To quantitatively rank instances in S, we define an impact measure based on the Information-gain Ratio (IR). We calculate IRi between attribute Ai and C, and use IRi as the impact-sensitive weight of Ai. The sum of impact-sensitive weights from all located erroneous attributes of Ik indicates its total impact value. The experimental results demonstrate the effectiveness of our strategies.
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.
14162405
Dynamic classifier selection for effective mining from noisy data streams
Mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single "best" classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence.
14899560
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they require strategies different from those of predictable attributes. This paper presents a study on identifying, cleansing and measuring noise for predictive-but-unpredictable attributes. New strategies are accordingly proposed. Both theoretical analysis and empirical evidence suggest that these strategies are more effective and more efficient than previous alternatives.
2952964
Non-Disjoint Discretization for Naive-Bayes Classifiers
Previous discretization techniques have discretized numeric attributes into disjoint intervals. We argue that this is neither necessary nor appropriate for naive-Bayes classifiers. The analysis leads to a new discretization method, Non-Disjoint Discretization (NDD). NDD forms overlapping intervals for a numeric attribute, always locating a value toward the middle of an interval to obtain more reliable probability estimation. It also adjusts the number and size of discretized intervals to the number of training instances, seeking an appropriate trade-off between bias and variance of probability estimation. We justify NDD in theory and test it on a wide cross-section of datasets. Our experimental results suggest that for naiveBayes classifiers, NDD works better than alternative discretization approaches.
295949
A Study of Approaches to Hypertext Categorization
Hypertext poses new research challenges for text classification. Hyperlinks, HTML tags, category labels distributed over linked documents, and meta data extracted from related Web sites all provide rich information for classifying hypertext documents. How to appropriately represent that information and automatically learn statistical patterns for solving hypertext classification problems is an open question. This paper seeks a principled approach to providing the answers. Specifically, we define five hypertext regularities which may (or may not) hold in a particular application domain, and whose presence (or absence) may significantly influence the optimal design of a classifier. Using three hypertext datasets and three well-known learning algorithms (Naive Bayes, Nearest Neighbor, and First Order Inductive Learner), we examine these regularities in different domains, and compare alternative ways to exploit them. Our results show that the identification of hypertext regularities in the data and the selection of appropriate representations for hypertext in particular domains are crucial, but seldom obvious, in real-world problems. We find that adding the words in the linked neighborhood to the page having those links (both inlinks and outlinks) were helpful for all our classifiers on one data set, but more harmful than helpful for two out of the three classifiers on the remaining datasets. We also observed that extracting meta data from related Web sites was extremely useful for improving classification accuracy in some of those domains. Finally, the relative performance of the classifiers being tested provided insights into their strengths and limitations for solving classification problems involving diverse and often noisy Web pages.
6583750
Building Trust in Online Rating Systems Through Signal Modeling
Online feedback-based rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but difficult problem. This problem is challenging especially when the number of regular ratings is relatively small and the unfair ratings contribute to a significant portion of the overall ratings. In this paper, we propose a novel algorithm to detect the unfair ratings that cannot be effectively prevented by existing state-of-the-art techniques. Our algorithm is particularly effective to detect malicious raters that collaboratively manipulate ratings of one or several products. The main idea of our algorithm is to use an autoregressive signal modeling technique combined with trust-enhanced rating aggregation. We are able to detect and filter out unfair ratings very accurately. Extensive experiments through simulations and real-world data have been performed to validate the proposed algorithm. The experimental results show significant improvements on detecting collaborative unfair raters over existing techniques.
2707132
Resource constraints analysis of workflow specifications
A workflow specification is a formal description of business processes in the real world. Its correctness is critical to the workflow execution and hence the realisation of business objectives. In addition to structural and temporal constraints, resource constraints are also implied in workflow specifications. Therefore, they should be analysed to ensure that the workflow specification is resource consistent at build-time. In this paper, we first identify the problem of resource constraints in a workflow specification. Then we propose an innovative approach with corresponding algorithms to the checking of resource consistency for a workflow specification. Furthermore, we extend our analysis work to timed workflow specifications, where time information is taken into consideration for the checking of the resource consistency of a workflow specification. The work reported in this paper provides a theoretical foundation for workflow modeling and analysis in workflow management.
12902641
A compact improved quadtree representation with image manipulations
Abstract In this paper, a new and compact improved quadtree (compact-IQ) representation is presented for representing binary images. Then, efficient set operations on compact-IQs are also presented. Some experimentations are carried out to demonstrate the memory-efficiency and computational advantages of the proposed method. The experimental results reveal that the proposed image representation has 11.04–18.80% compression improvement and 24.39–36.94% computation-time improvement for set operations when compared to the recently published method on the constant bit-length linear quadtrees (CBLQ) (T.W. Lin, Set operations on constant bit-length linear quadtrees, Pattern Recognition 30(7) (1997) 1239–1249). In addition, geometric operations (area and centroid) on the compact-IQ and the performance comparison with JBIG are also investigated.
2958357
An Architecture and the Related Mechanisms for Web-based Global Cooperative Teamwork Support
Given the exposure of the Internet and the Web, there is a significant impact on Web-based cooperative teamwork support which can be beneficial to many teamwork managers and normal team members who may be either computing or non-computing professionals. In this paper, we focus on our research into a more effective architecture for Web-based teamwork automation support and its corresponding innovative mechanisms for various perspectives including visual process modelling for teamwork managers and process enactment for team members in an asynchronous and synchronous manner. Our research prototype is implemented in Java and data repository used can be an object-oriented or relational database.