1232229
Active Mining of Data Streams
Most previously proposed mining methods on data streams make an unrealistic assumption that “labelled” data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are refreshed periodically, that is usually synchronized with data availability schedule. There are several undesirable consequences of this “passive periodic refresh”. In this paper, we propose a new concept of demand-driven active data mining. It estimates the error of the model on the new data stream without knowing the true class labels. When significantly higher error is suspected, it investigates the true class labels of a selected number of examples in the most recent data stream to verify the suspected higher error. 1 State-of-the-art Stream Mining State-of-the-art work on mining data streams concentrates on capturing time-evolving trends and patterns with “labeled” data. However, one important aspect that is often ignored or unrealistically assumed is the availability of “class labels” of data streams. Most algorithms make an implicit and impractical assumption that labeled data is readily available. Most works focus on how to detect the change in patterns and how to update the model to reflect such changes when there are “labelled” instances to be learned. However, for many applications, the class labels are not “immediately” available unless dedicated efforts and substantial costs are spent to investigate these labels right away. If the true class labels were readily available, data mining models would not be very useful we might just wait. In credit card fraud detection, we usually do not know if a particular transaction is a fraud until at least one month later after the account holder receives and reviews the monthly statement. Due to these facts, most current applications obtain class labels and update existing models in preset frequency, usually synchronized with data refresh. The effectiveness of the passive mode is dictated by some “statuary and static constraints”, yet not by the “demand” for a better model with a lower loss. Such a passive mode to mine data streams results in a number of potential undesirable consequences that contradict the notions of “streaming” and “continuous”. First, it may incur possibly higher loss due to neglected pattern drifts. If either the concept or data distribution drifts rapidly at an un-forecasted rate that statuary constraints do not catch up, the models are likely to be out-of-date on the data stream and important business opportunities might be missed. Second, it may have unnecessary model refresh. If there is neither conceptual nor distributional change, periodic passive model refresh and re-validation is a waste of resources. 1.1 Demand-driven Active Mining of Data Streams We are proposing a demand-driven active stream data mining process that solves the problems of passive stream data mining. As a summary, our particular implementation of active stream data mining has three simple steps: 1. Detect potential changes of data streams “on the fly” when the existing model classifies continuous data streams. The detection process does not use or know any true labels of the stream. One of the change detection methods is a “guess” of the actual loss or error rate of the model on the new data stream. 2. If the guessed loss or error rate of the model in step 1 is much higher than an application-specific tolerable maximum, we choose a small number of data records in the new data stream to investigate their true class labels. With these true class labels, we statistically estimate the true loss of the model. 3. If the statistically estimated loss in step 2 is verified to be higher than the tolerable maximum, we reconstruct the old model by using the same true class labels sampled in the previous step. In this paper, we concentrate on the first two steps. Our particular implementation extends on classification trees.
5977047
Decision tree evolution using limited number of labeled data items from drifting data streams
Most previously proposed mining methods on data streams make an unrealistic assumption that "labelled" data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are reconstructed only when labelled data become available periodically. This passive stream mining model has several drawbacks. We propose a concept of demand-driven active data mining. In active mining, the loss of the model is either continuously guessed without using any true class labels or estimated, whenever necessary, from a small number of instances whose actual class labels are verified by paying an affordable cost. When the estimated loss is more than a tolerable threshold, the model evolves by using a small number of instances with verified true class labels. Previous work on active mining concentrates on error guess and estimation. In this paper, we discuss several approaches on decision tree evolution.
2832131
Applying Data Mining to Pseudo-Relevance Feedback for High Performance Text Retrieval
In this paper, we investigate the use of data mining, in particular the text classification and co-training techniques, to identify more relevant passages based on a small set of labeled passages obtained from the blind feedback of a retrieval system. The data mining results are used to expand query terms and to re-estimate some of the parameters used in a probabilistic weighting function. We evaluate the data mining based feedback method on the TREC HARD data set. The results show that data mining can be successfully applied to improve the text retrieval performance. We report our experimental findings in detail.
2029604
QoS-based resource discovery in intermittently available environments
In this paper, we address the problem of resource discovery in a grid based multimedia environment, where the resources providers, i.e. servers, are intermittently available. Given a graph theoretic approach, we define and formulate various policies for QoS-based resource discovery with intermittently available servers that can meet a variety of user needs. We evaluate the performance of these policies under various time-map scenarios and placement strategies. Our performance results illustrate the added benefits obtained by adding flexibility to the scheduling process.
10038411
On personal data license design and negotiation
Web applications often require users to provide personal information for customization, identification, or completing a transaction. However, the way we disclose our information in current cyberspace does not allow us to control the usage, release and circulation of the information, violating the basic principle of personal information privacy. Online personal data licensing (OPDL) is a framework to bring the control of personal information back to individuals by requiring application and service providers to obtain a license from a person before collecting, processing, and using his personal information. In this paper we present the license design and automation of the license negotiation process.
15221020
Learning Declarative Control Rules for Constraint-BAsed Planning
Despite the long history of research in using machine learning to speed-up state-space planning, the techniques that have been developed are not yet in widespread use in practical planning systems. One limiting factor is that traditional domain-independent planning systems scale so poorly that extensive learned control knowledge is required to raise their performance to an acceptable level. Therefore, work in this area has focused on learning large numbers of control rules that are specific to the details of the underlying planning algorithms, which can be extremely costly. In recent years, a new generation of planning systems with much improved speed and scalability has become available. These systems formulate planning as solving a large constraint satisfaction problem. This formulation opens up the possibility that domain-specific control knowledge can be added to the planner in a purely declarative manner via a set of additional constraints. In this paper we present the first positive results on automatically acquiring such high-level, declarative constraints using machine learning techniques. In particular, we will show that a new heuristic method for generating training examples together with a rule induction algorithm can learn useful control rules in a variety of domains. Only a small number of rules are needed to reduce solution times by two orders of magnitude or more on larger problems, training times are short, and the learned rules can be exported to other planning systems.
8485722
The maximum genus of graphs with diameter three
Abstract This paper shows that if G is a simple graph with diameter three then G is up-embeddable unless G is either a Δ2-graph (Fig. 1) or a Δ3-graph (Fig. 2) with ξ(G) = 2, i.e., the maximum genus γM(G) = (β(G) − 2)/2.