zahra ahmadi | Isfahan University of Technology (original) (raw)
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Papers by zahra ahmadi
Evolving Systems, 2012
Data streams have some unique properties which make them applicable in precise modeling of many r... more Data streams have some unique properties which make them applicable in precise modeling of many real data mining applications. The most challenging property of data streams is the occurrence of "concept drift". Recurring concepts is a type of concept drift which can be seen in most of real world problems. Detecting recurring concepts makes it possible to exploit previous knowledge obtained in the learning process. This leads to quick adaptation of the learner whenever a concept reappears. In this paper, we propose a learning algorithm called Pool and Accuracy based Stream Classification (PASC), which takes the advantage of maintaining a pool of classifiers to track recurring concepts. Each classifier is used to describe an existing concept. Two methods are presented for classification task: active classifier and weighted classifiers methods. For the updating of the pool we use two methods: Bayesian and Heuristic. Experimental results on real and artificial datasets show the effectiveness of weighted classifiers method while dealing with sudden concept drifting datasets. In addition, the proposed updating methods outperform the existing algorithms in datasets with arbitrary attributes.
Evolving Systems, 2012
Data streams have some unique properties which make them applicable in precise modeling of many r... more Data streams have some unique properties which make them applicable in precise modeling of many real data mining applications. The most challenging property of data streams is the occurrence of "concept drift". Recurring concepts is a type of concept drift which can be seen in most of real world problems. Detecting recurring concepts makes it possible to exploit previous knowledge obtained in the learning process. This leads to quick adaptation of the learner whenever a concept reappears. In this paper, we propose a learning algorithm called Pool and Accuracy based Stream Classification (PASC), which takes the advantage of maintaining a pool of classifiers to track recurring concepts. Each classifier is used to describe an existing concept. Two methods are presented for classification task: active classifier and weighted classifiers methods. For the updating of the pool we use two methods: Bayesian and Heuristic. Experimental results on real and artificial datasets show the effectiveness of weighted classifiers method while dealing with sudden concept drifting datasets. In addition, the proposed updating methods outperform the existing algorithms in datasets with arbitrary attributes.