Abstract
In document-level text mining, feature selection is crucial for lowering ambiguity which in turn enhances classifier performance. The selection of the vital features is crucial, especially for the classification of documents in the morphologically rich Indian regional language Kannada. In this regard, the paper proposes stacked ensemble feature selection method. The proposed method consists of two layers, and it is a heterogeneous ensemble of feature selection methods. In the first layer, Chi-Square and Mutual Information methods are combined. In the second layer, we have XG Boost. These two layers select prominent features and enhance the classifier learning performance. Prominent classifiers like Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), and Decision Tree (DT) are used in experiments. Further K-Fold experimentations are performed, and their results are analyzed.
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Rangan, R.K., Harish, B.S., Roopa, C.K. (2024). Stacked Ensemble Feature Selection Method for Kannada Documents Categorization. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 786. Springer, Singapore. https://doi.org/10.1007/978-981-99-6547-2_33
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DOI: https://doi.org/10.1007/978-981-99-6547-2_33
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