A plug-in feature extraction and feature subset selection algorithm for classification of medicinal brain image data
A. Veeramuthu, S. Meenakshi, A. Kameshwaran · 2014
In pattern recognition and in image processing, feature extraction is a special type of dimensionality reduction. In data mining, Attribute subset selection or feature subset selection is normally helps for data reduction by removing unrelated and redundant dimensions. Given a set of image data features are extracted. From the extracted features, feature subset selection finds the subset of features that are most relevant to data mining task. The efficiency and effectiveness of the feature selection algorithm is evaluated. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the proportion of the selected features. Based on these criteria, we have used Spatial Gray Level Difference Method (SGLDM) feature extraction algorithm and Correlation based Feature Selection (CFS). Projected Classification algorithm (PROCLASS) is going to be proposed for brain image data. Experiments are going to do compare these plug-in algorithms with FAST, FCBF feature selection algorithms.