Manoranjan Dash

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Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a comprehensive overview of many existing methods from the(More)
Feature selection is an effective technique in dealing with dimensionality reduction. For classification, it is used to find an “optimal” subset of relevant features such that the overall accuracy of classification is increased while the data size is reduced and the comprehensibility is improved. Feature selection methods contain two important aspects:(More)
Discrete values have important roles in data mining and knowledge discovery. They are about intervals of numbers which are more concise to represent and specify, easier to use and comprehend as they are closer to a knowledge-level representation than continuous values. Many studies show induction tasks can benefit from discretization: rules with discrete(More)
Clustering is an important data mining task Data mining often concerns large and high dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high dimensionality or both Di erent features a ect clusters di erently some are important for clusters while others may hinder the clustering task An e(More)
Fuzzy clustering is capable of finding vague boundaries that crisp clustering fails to obtain. But time complexity of fuzzy clustering is usually high, and the need to specify complicated parameters hinders its use. In this paper, an entropy-based fuzzy clustering method is proposed. It automatically identifies the number and initial locations of cluster(More)
Feature selection is an e ective technique in dealing with dimensionality reduction for classi cation task a main component of data mining It searches for an optimal subset of features The search strategies under consideration are one of the three complete heuristic and probabilistic Existing algorithms adopt various measures to evaluate the goodness of(More)
Processing applications with a large number of dimensions has been a challenge to the KDD community. Feature selection, an effective dimensionality reduction technique, is an essential pre-processing method to remove noisy features. In the literature there are only a few methods proposed for feature selection for clustering. And, almost all of those methods(More)
Microarray technologies enable quantitative simultaneous monitoring of expression levels for thousands of genes under various experimental conditions. This new technology has provided a new way of biological classification on a genome-wide scale. However, predictive accuracy is affected by the presence of thousands of genes many of which are unnecessary(More)
This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds(More)