Displaying similar documents to “Toward Optimal Feature Selection Using Ranking Methods and Classification Algorithms”

Combined classifier based on feature space partitioning

Michał Woźniak, Bartosz Krawczyk (2012)

International Journal of Applied Mathematics and Computer Science

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This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier,...

Improving feature selection process resistance to failures caused by curse-of-dimensionality effects

Petr Somol, Jiří Grim, Jana Novovičová, Pavel Pudil (2011)

Kybernetika

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The purpose of feature selection in machine learning is at least two-fold - saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality - feature selection methods may...

Experiments with two Approaches for Tracking Drifting Concepts

Koychev, Ivan (2007)

Serdica Journal of Computing

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This paper addresses the task of learning classifiers from streams of labelled data. In this case we can face the problem that the underlying concepts can change over time. The paper studies two mechanisms developed for dealing with changing concepts. Both are based on the time window idea. The first one forgets gradually, by assigning to the examples weight that gradually decreases over time. The second one uses a statistical test to detect changes in concept and then optimizes the...

Rough sets methods in feature reduction and classification

Roman Świniarski (2001)

International Journal of Applied Mathematics and Computer Science

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The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. The paper also contains a description of the algorithm for feature selection and reduction based on...

Conceptual base of feature selection consulting system

Pavel Pudil, Jana Novovičová, Petr Somol, Radek Vrňata (1998)

Kybernetika

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The paper briefly reviews recent advances in the methodology of feature selection (FS) and the conceptual base of a consulting system for solving FS problems. The reasons for designing a kind of expert or consulting system which would guide a less experienced user are outlined. The paper also attempts to provide a guideline which approach to choose with respect to the extent of a priori knowledge of the problem. The methods discussed here form the core of the software package being developed...