Abstract
Feature selection is an effective technique in dealing with dimensionality reduction. Identifying relevant feature in the dataset and discarding everything else as irrelevant and redundant can improve the performance of classifier. Algorithm for feature selection fall into three broad techniques: wrappers use the learning algorithm itself to evaluate the usefulness of feature, embedded is built into the classifier construction, while filters assess the relevance of features by looking only at the intrinsic properties of the data. For application to large databases, filters technique have proven to be more practical than others because they are much faster. However, their performance is worse than others when the classifiers are combined. In this study we present a general framework for creating several feature subsets and then combine them into a single subset. A new combiner is proposed for selecting features to improve the performance of filter techniques that exist. Experiment results demonstracted that the new combiner approach gives the significicant improvement for k- nearest neighbor classifier, especially using on quantitative data. Finally, the proposed method was employed to analyze the TFT-LCD array process inspection. Implementation results showed that the test items have been significantly reduced and the performance has been improved.