Abstract
In text classification feature selection is used to reduce the feature space to improve the classification accuracy. In this paper, we propose a method max-min document frequency-based feature selection and we applied Extreme Learning Machine (ELM) model to improvise the text classification performance. For this text classification, we used the multilabel Reuters dataset which consists of 10788 number of documents. In this experiment, the ELM model performs better using max-min document frequency-based feature selection in terms of precision, recall, and F-measure as is compared to the ELM model using full feature space without using any feature selection technique.
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Behera, S.K., Dash, R. (2021). Performance of ELM Using Max-Min Document Frequency-Based Feature Selection in Multilabeled Text Classification. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 194. Springer, Singapore. https://doi.org/10.1007/978-981-15-5971-6_46
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DOI: https://doi.org/10.1007/978-981-15-5971-6_46
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