Techniques for Selecting the Optimal Parameters of One-Class Support Vector Machine Classifier for Reduced Samples

1 Introduction

In statistics or data mining, a typical task is to learn a model from available data. The problem with evaluating such a model is that it may demonstrate adequate prediction capability on the training data, but might fail to predict future unseen data. Also, the validation protocol is the most procedure for estimating the generalization performance in this context.

Generally, cross-validation is a statistical method of evaluating and comparing the training model by dividing data into two subsets; one is used for training and the other is used for evaluating the model. In typical cross-validation, the training and validation sets must cross over in successive rounds such that each data point has a chance of being validated against. The basic form of cross-validation is k-fold cross-validation. Other forms of cross-validation are special cases of k-fold cross-validation or involve repeated rounds of k-fold cross-validation, first appeared by Mosteller and Turkey. (Mosteller & Tukey, 1968)

In 1970, both Stone (Stone, 1974) and Geisser (Geisser, 1975) employed cross-validation. Currently, cross-validation is widely accepted in machine learning as a standard procedure for performance evaluation. There are two possible goals in cross-validation. The first goal is to estimate the performance of the learned model from available data using one algorithm. The second goal is to compare the performance of two or more different algorithms and find out the best algorithm for the available data, or alternatively to compare the performance of two or more variants of the parameterized model.