Optimizing Feature Selection Of Svm Using Genetic Algorithm
The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data so as to obtain acceptable classification accuracy within reasonable time. Selecting better feature subsets can reduce the computational cost of feature measurement, increase classifier efficiency, and...
Paperback: 96 pages
Publisher: LAP LAMBERT Academic Publishing (May 19, 2017)
Product Dimensions: 5.9 x 0.2 x 8.7 inches
Format: PDF ePub TXT book
- 3659902489 pdf
- 978-3659902482 pdf
- Temitayo Fagbola pdf
- Temitayo Fagbola ebooks
- epub books
Campbell biology get ready for biology mastering biology with etext and access card 10th edition Download Manana iguana pdf at centrodistsaramont.wordpress.com Read Writing the romantic comedy the art and craft of writing screenplays that sell ebook alltunmiaf.wordpress.com
allow greater classification accuracy based on the process of deriving new features from the original features.In this study, a Genetic Algorithm-based feature selection technique is proposed in order to reduce the number of feature subsets to be classified by SVM, optimize the classification parameters as well as the prediction accuracy and computation time of the SVM classifier so that a marked improvement can be obtained over raw classification. Spam assassin dataset was used in this study to validate the performance of the proposed system. The hybrid GA-SVM developed has shown a remarkable improvement over SVM in terms of classification accuracy and computation time.
Leave a Comment