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1

Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach

Delimata P., Suraj Z.

Fundamenta Informaticae

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2008

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Vol. 85, nr 1-4

97-110

EN

Many problems in pattern classification and knowledge discovery require a selection of a subset of attributes or features to represent the patterns to be classified. The approach presented in this paper is designed mostly for multiple classifier systems with homogeneous (identical) classifiers. Such systems require many different subsets of the data set. The problem of finding the best subsets of a given feature set is of exponential complexity. The main aim of this paper is to present ways to improve RBFS algorithm which is a feature selection algorithm. RBFS algorithm is computationally quite complex because it uses all decision-relative reducts of a given decision table. In order to increase its speed, we propose a new algorithm called ARS algorithm. The task of this algorithm is to decrease the number of the decision-relative reducts for a decision table. Experiments have shown that ARS has greatly improved the execution time of the RBFS algorithm. A small loss on the classification accuracy of the multiple classifier used on the subset created by this algorithm has also been observed. To improve classification accuracy the simplified version of the bagging algorithm has been applied. Algorithms have been tested on some benchmarks.

2

On k-NN Method with Preprocessing

Suraj Z., Delimata P.

Fundamenta Informaticae

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2006

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Vol. 69, nr 3

343-358

EN

The objective of this study is to introduce a new model of data classification based on preliminary reduction of the training set of examples (preprocessing) in order to facilitate the use of nearest neighbours (NN) techniques in near real-time applications. This study accordingly addresses the issue of minimising the computational resource requirements of NN techniques, memory as well as time. The approach proposed in the paper is a modification of the classical k-Nearest Neighbours (k-NN) method and the k-NN method with local metric induction. Generally, the k-NN method with local metric induction in comparison with the classical k-NN method gives better results in the classification of new examples. Nevertheless, for the large data sets the k-NN method with local metric induction is less time effective than the classical one. The time/space efficiency of classifying algorithms based on these two methods depends not only on a given metric but also on the size of training data. In the paper, we present three methods of preliminary reduction of the training set of examples. All reduction methods decrease the size of a given experimental data preserving the relatively high classification accuracy. Results of experiments conducted on well known data sets, demonstrate the potential benefits of such reduction methods.

3

A Rough Set Approach to Multiple Classifier Systems

Suraj Z., El Gayar N., Delimata P.

Fundamenta Informaticae

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2006

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Vol. 72, nr 1-3

393-406

EN

During the past decade methods of multiple classifier systems have been developed as a practical and effective solution for a variety of challenging applications. A wide number of techniques and methodologies for combining classifiers have been proposed in the past years in literature. In our work we present a new approach to multiple classifier systems using rough sets to construct classifier ensembles. Rough set methods provide us with various useful techniques of data classification. In the paper, we also present a method of reduction of the data set with the use of multiple classifiers. Reduction of the data set is performed on attributes and allows to decrease the number of conditional attributes in the decision table. Our method helps to decrease the number of conditional attributes of the data with a small loss on classification accuracy.