Project Dissertation Report

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Feature Extraction plays a critical role in better performance of the classifier.

We address the dimension reduction of DNA features in which relevant features are extracted among thousands of irrelevant ones through dimensionality reduction.

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This technique is implemented on microarray cancer data to select training data using multiobjective genetic algorithm with non-dominated sorting (MOGA-NSGA-II).

The two objective functions for this multiobjective techniques are optimization of cluster compactness as well as separation simultaneously. the individual chromosome which gives the optimal value of the compactness and separation.

Then we find high con dence points for these non-dominated set using a fuzzy voting technique.

Support Vector Machine (SVM) classifier is further trained by the selected training points which have high confidence value.


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