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ArK Feature Selection Algorithm to resolve Small Sample Size Problem

L. Arockiam, V. Arul Kumar


Dimensionality Reduction (DR) is an important technique which is used to reduce the dimensionality of features present in the datasets. This technique is used in various fields such as Data Mining, Machine Learning, Pattern Recognition, Image Retrieval, Text mining etc. In the data mining filed, DR is an important preprocessing technique. Linear Discriminant Analysis (LDA) is a popular DR technique. Traditional LDA technique faces a Small Sample Size (SSS) problem. The SSS problem occurs when the number of samples is less than the dimensionality of the samples. A Lot of feature selection algorithms are proposed in the earlier days, but still the problem persists. Hence, a new feature selection algorithm is proposed in this paper to overcome the SSS problem.


Feature Selection, Filter Approach, Fisher Criterion, Feature Selection Algorithm

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