Abstract
The continuous increase of information in the web with varying dimensions is becoming difficult for users to filter and analyse them efficiently as it incorporates redundant and irrelevant terms. Managing, filtering and organizing such huge datasets need the classification of text documents to be performed. Text classification is the process of assigning the text documents to their predefined text categories based on the content. The aim of this paper is to explore Cuckoo search optimization (CSO) problem established from the behaviour of cuckoo birds for selection of relevant features by modifying the algorithm. The revised algorithm is named as modified Cuckoo search (MCS) optimization algorithm that can be proved to be useful for developing an efficient text classification system. The proposed method is generated by combining the ability of MCS with the sharpness of Naive Bayes Multinomial (NBM) algorithm for generating proper feature which increases the rate of success. The approach adopted here is tested on 9000 text documents that cover eight different domains fetched from several web sources and obtains encouraging outcome. The results compared with the results from other well-known approaches for text classification task show the effectiveness of the proposed approach as an automatic Bangla text classification system.
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One of the authors thank DST for the INSPIRE fellowship and also thank various links provided in [7] from which the data has been collected.
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Dhar, A., Dash, N.S., Roy, K. (2019). Efficient Feature Selection Based on Modified Cuckoo Search Optimization Problem for Classifying Web Text Documents. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_57
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