Feature Selection with Fuzzy Decision Reducts

In this paper, within the context of fuzzy rough set theory, we generalize the classical rough set framework for data-based attribute selection and reduction, based on the notion of fuzzy decision reducts. Experimental analysis confirms the potential of the approach

Measures for unsupervised fuzzy-rough feature selection

For supervised learning, feature selection algorithms at-tempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is therefore intuitive to re-tain only those features that are related to or lead to these decision classes. However, in unsupervised learning, deci-sion class labels are not provided, which poses questions such as; which features should be retained? and, why not use all of the information? The problem is that not all fea-tures are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, some new fuzzy-rough set-based approaches to unsupervised fea-ture selection are proposed. These approaches require no thresholding or domain information, can operate on real-valued data, and result in a significant reduction in dimen-sionality whilst retaining the semantics of the data. 1

Selected approaches for decision rules construction-comparative study

Decision rules are popular form of knowledge representation. From this point of view, length of such rules is an important factor since it influences on data understanding by experts. Unfortunately, the problem of construction of short rules is NP-hard, so different heuristics are discussed in the literature. The paper presents comparison of two selected methods for decision rules construction. The first one is connected with a new algorithm based on EAV model, the second one - with construction of rules based on reduct. Decision rules were induced for data sets from UCI ML Repository and compared from the point of view of length and support, and from the point of view of classification accuracy. Results of Wilcoxon test are also included

Reduct-based ranking of attributes

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles