SVM BASED GA TEXT CLASSIFICATION
- Geetanjali kukade
- Dharmendra sharma
SVM based GA, Text classification, information retrieval.
This paper proposed the use of Support Vector Machines (SVMs) for learning text classifier from examples. It analyzes the particular properties of learning with text data and ideates. We proposed text classification which is based on genetic algorithm. There are other two open problems in text mining: polysemy and synonymy. Polysemy refers to the fact that a word can have multiple meanings. Distinguishing between different meanings of a word (called word sense disambiguation) is not easy. In order to explicitly capture the optimality of word clusters in an information theoretic framework we will apply the genetic algorithm SVM parameters modeling to social networking sites for text classification and our investigational outcome will illustrate that SVM based GA significantly better than Naïve Bayes and SVM, even when the data is noisy or partially labeled. Our work includes a proportional revision on classification of the data set with added machine learning algorithms such as support vector machines, genetic algorithm parameters. Since character genetic algorithm SVM parameters to be effective in text categorization, we plan to explore their competence in information retrieval tasks for agglutinative languages.
Volume 2 Issue 1
Pages. 700-703