Feature Selection using Genetic Algorithm to improve SVM Classifier

Language: English

Published by LAP LAMBERT Academic Publishing Jan 2019, 2019

6139992079 / 9786139992072

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This item is printed on demand - Print on Demand Titel. Neuware -This book gives a classification algorithms like Support Vector Machine and Genetic Algorithm are used to find the classification accuracy for the Wisconsin Breast Cancer dataset. The benchmark dataset, Wisconsin Breast Cancer dataset is obtained from UCI Machine Learning Repository. The dataset consists of 699 instances divided into 2 classes namely Benign and Malignant, each with 11 attributes. Support vector machines (SVMs) are a set of related supervised learning methods used for classification. A classification SVM model attempts to separate the target classes with the widest possible margin. In SVM, Radial basis function and Polynomial kernel function are used to calculate classification accuracy and run time. Feature Selection is used to improve the accuracy of the SVM classifier.In GA, Integer and Binary Coded Genetic Algorithm are also used to calculate classification accuracy and run time. Integer- Coded Genetic Algorithm is used to select important and relevant features for classification. Binary Coded Genetic Algorithm can be applied to many optimization problems which contains binary string for the variables.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.

Seller Inventory # 9786139992072

Title
Feature Selection using Genetic Algorithm to improve SVM Classifier
Author
Nithya Devaraj
Publisher
LAP LAMBERT Academic Publishing Jan 2019
Publication year
2019
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6139992079
ISBN 13
9786139992072
Item weight
173 grams
Dimensions
220x150x7 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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