An incremental learning approach for the text categorization using hybrid optimization
| Autor: | Mamta Kayest, Sanjay Kumar Jain |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Matching (statistics) General Computer Science Artificial neural network Process (engineering) Computer science business.industry 05 social sciences Search engine indexing Feature extraction 02 engineering and technology Machine learning computer.software_genre Term (time) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Bhattacharyya distance 020201 artificial intelligence & image processing Artificial intelligence Document retrieval business computer |
| Zdroj: | International Journal of Intelligent Computing and Cybernetics. 12:333-351 |
| ISSN: | 1756-378X |
| DOI: | 10.1108/ijicc-12-2018-0170 |
| Popis: | Purpose Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents. Design/methodology/approach This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF based NN). Initially, the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency (TF–IDF) and holoentropy to find the keywords of the document. Then, cluster-based indexing is performed using MB–FF algorithm, and finally, by matching process with the modified Bhattacharya distance measure, the document retrieval is done. In MB–FF based NN, the weights in the NN are chosen using MB–FF algorithm. Findings The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769, recall value of 0.7957, F-measure of 0.8143 and accuracy of 0.7815, respectively. Originality/value The experimental results show that the proposed MB–FF based NN is useful to companies, which have a large workforce across the country. |
| Databáze: | OpenAIRE |
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