Motivation: Biological research produces a wealth of measured data. Neither it is easy for biologists to postulate hypotheses about the behaviour or structure of the observed entity because the relevant properties measured are not seen in the ocean of measurements. Nor it is easy to design machine learning algorithms to classify or cluster the data items for the same reason. Algorithms for auto...

Feature subset selection is the process of choosing a subset of good features with respect to the target concept. A clustering based feature subset selection algorithm has been applied over software defect prediction data sets. Software defect prediction domain has been chosen due to the growing importance of maintaining high reliability and high quality for any software being developed. A soft...

Feature subset selection (FSS) is a known technique to pre-process the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated data. We propose Corona, a simple yet effective supervised feature subset selection technique for Multivariate Time Series (MTS). T...

In the present paper, a novel method for Feature Subset Selection in dataset, FSS-TLBOA (Feature Subset Selection by Teaching Learning Based Optimization Algorithm), is proposed. A dataset can contain several features. Many Clustering methods are designed for clustering low–dimensional data. In high dimensional space finding clusters of data objects is challenging due to the curse of dimensiona...

A relevance filter is proposed which removes features based on the mutual information between class labels and features. It is proven that both feature independence and class conditional feature independence are required for the filter to be statistically optimal. This could be shown by establishing a relationship with the conditional relative entropy framework for feature selection. Removing f...

This paper describes an optimal approach for feature extraction and selection for classification of leaves based on Genetic Algorithm (GA). The selection of the optimal features subset and the classification has become an important methodology in the field of Leaf classification. The deterministic feature sequence is extracted from the leaf images using GA technique, and these extracted feature...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Feature Selection (FS) is applied to reduce the number of features in many applications where data has multiple features. FS is an essential step in successful data mining applications, which can effectively reduce data dimensionality by removing t...

In the high dimensional data set having features selection involves identifying a subset of the most useful features that produce compatible results as the original entire set of features. A fast algorithm may be evaluated from both the ability concerns the time required to find a subset of features and the value is required to the quality of the subset of features. Fast clustering based featur...

Fuzzy Set Theory and Rough Set Theory are the most popular mathematical tools for dealing with uncertainties. During past decades, these set theories are being applied successfully in several areas for solving many complex tasks. This paper is concerned with the application of hybrid Fuzzy-Rough set based approach for feature subset selection. Keywords— Fuzzy set theory, Rough Set theory, Fuzzy...

In building a response model, determining the inputs to the model has been an important issue because of the complexities of the marketing problem and limitations of mental models for decision-making. It is common that the customers’ historical purchase data contains many irrelevant or redundant features thus result in bad model performance. Furthermore, single complex models based on feature s...