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1

A Classifier Based on a Decision Tree with Verifying Cuts

Bazan J. G., Buregwa-Czuma S., Rzasa W., Bazan-Socha S., Dydo L., Skowron A.

Fundamenta Informaticae

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2016

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Vol. 143, nr 1/2

1--18

EN

This article introduces a new method of a decision tree construction. Such construction is performed using additional cuts applied for a verification of the cuts' quality in tree nodes during the classification of objects. The presented approach allows us to exploit the additional knowledge represented in the attributes which could be eliminated using greedy methods. The paper includes the results of experiments performed on data sets from a biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, well known from literature. Our new method out performs the existing method, which is also confirmed by statistical tests.

2

An Approach to Pattern Recognition Based on Hierarchical Granular Computing

Nguyen S.H., Nguyen T.T., Szczuka M., Nguyen H.S.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

369--384

EN

This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last 10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification.

3

A Domain Knowledge as a Tool For Improving Classifiers

Bazan J.G., Buregwa-Czuma S., Jankowski A. W.

Fundamenta Informaticae

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2013

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Vol. 127, nr 1-4

495--511

EN

This paper investigates the approaches to an improvement of classifiers quality through the application of a domain knowledge. The expertise may be utilizable on several levels of decision algorithms such as: feature extraction, feature selection, a definition of temporal patterns used in an approximation of the concepts, especially of the complex spatio-temporal ones, an assignment of an object to the concept and a measurement of the objects similarity. The domain knowledge incorporation results then in the reduction of the size of searched spaces. The work constitutes an overview of classifier building methods efficiently utilizing the expertise, worked out latterly by Professor Andrzej Skowron research group. The methods using domain knowledge intended to enhance the quality of classic classifiers, to identify the behavioral patterns and for automatic planning are discussed. Finally it answers a question whether the methods satisfy the hopes vested in them and indicates the directions for future development.

4

Concept Approximations Based on Rough Sets and Similarity Measures

Saquer J., Deogun J. S.

International Journal of Applied Mathematics and Computer Science

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2001

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Vol. 11, no 3

655-674

EN

The formal concept analysis gives a mathematical definition of a formal concept. However, in many real-life applications, the problem under investigation cannot be described by formal concepts. Such concepts are called the non-definable concepts (Saquer and Deogun, 2000b). The process of finding formal concepts that best describe non-definable concepts is called the concept approximation. In this paper, we present two different approaches to the concept approximation. The first approach is based on rough set theory while the other is based on a similarity measure. We present algorithms for the two approaches.