计算机科学
分类
匹配(统计)
人工智能
搜索引擎索引
文档聚类
情报检索
度量(数据仓库)
文件分类
tf–国际设计公司
文本分类
价值(数学)
期限(时间)
数据挖掘
机器学习
聚类分析
统计
数学
物理
量子力学
作者
Mamta Kayest,Sanjay Kumar Jain
出处
期刊:International Journal of Intelligent Computing and Cybernetics
[Emerald (MCB UP)]
日期:2019-08-12
卷期号:12 (3): 333-351
被引量:5
标识
DOI:10.1108/ijicc-12-2018-0170
摘要
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.
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