Soft set theory proposed by Molodtsov is a new mathematical tool for dealing with the uncertain data. Soft set-based dimensionality reduction can be considered as a technique for feature selection. In this paper, we present an alternative technique for feature selection of traditional Malay musical instrument sounds dataset. The technique is based on matrices computation of multi-soft sets for ...

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the ...

The aim of text classification is to classify the text documents into a set of pre-defined categories. But the complexity of natural languages, high dimensional feature space and low quality of feature selection become the main problem for text classification process. Hence, in order strengthen the classification technique, selection of important features, and consequently removing the unimport...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

according to this fact that wind is now a part of global energy portfolio and due to unreliable and discontinuous production of wind energy; prediction of wind power value is proposed as a main necessity. in recent years, various methods have been proposed for wind power prediction. in this paper the prediction structure involves feature selection and use of artificial neural network (ann). in ...

Feature Selection is a preprocessing technique with great significance in data mining applications that aims at reducing computational complexity and increase predictive capability of a learning system. This paper presents a new hybrid feature selection algorithm based on Discrete Firefly optimization technique with dynamic alpha and gamma parameters and t-test filter technique to improve detec...

This work presents the method to classify the gene expression cancer data –Microarray data. The proposed method combines two techniques: classification and feature selection. The classification technique used in this work is Genetic Algorithm (GA) and the feature selection technique is Signal-to-Noise Ratio (SNR). Lymphoma and Leukemia datasets are used to test the performance of the proposed m...