In DNA microarray research, the increase in gene expression samples and feature dimensions become a challenge for feature selection. This makes it necessary that a more efficient and improved classification algorithm be developed so as to select optimal features in gene expression data. This study presents a new feature selection algorithm that combines the Correlation Feature Selection (CFS) and the Velocity Clamping Particle Swarm Optimization (VCPSO) algorithm. This hybrid model takes advantage of both the filters and the wrappers. It also selects the subsets with optimal features to classify genes by using different classifiers such as Support Vector Machine (SVM), Random Forest(RF),Naïve Bayes(NB) and Decision Tree(DT). Two bioinformatics problems become the basis of evaluation for hybrid mechanisms. These are neurodegenerative brain disorder protein data and microarray cancer data. Reducing the redundancy and finding optimal gene features is the need of the hour. Our experiments show that CFS-VCPSO-SVM selection method eliminates the redundant features and classifies the gene expression data with maximum accuracy.
Feature extraction is an important component of a pattern recognition system. A well-defined feature extraction algorithm makes the identification process more effective and efficient. Quality checking is one of the most prominent steps in many applications using Feature extraction. Several techniques exist for the quality checking of wooden materials. However, image based quality checking of wooden materials still remains a challenging task. Although trivial quality checking methods are available, they do not give useful results in most situations. This paper addresses the issue of quality checking of wooden materials using feature extraction techniques with high accuracy and reliability. Experiments conducted under the proposed conditions showing significant results are presented.
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