Abstract
Feature selection is an important task in machine learning. It can improve classification accuracy and effectively reduce the dataset dimensionality by removing no discriminative features. Though a large body of researches were focused on feature selection for text classification, few works addressed the problem for multi-label data in big data context. Therefore, this paper proposes a distributed feature selection approach for multi-label textual big data based on the weighted chi-square method. First, a standard multi-label approach to transform the multi-label data into single-label data is applied. Then, the algorithm assigns different weights to the features based on the category term frequency and then calculates the chi-square based on the weight of each feature. The proposed method is implemented on Hadoop framework using MapReduce programming model. At last, a set of experiments were conducted on three benchmarking text datasets to evaluate the effectiveness of the proposed approach. A comparative analysis of the results with the state-of-the-art techniques proves that our method is efficient, robust and scalable.
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Amazal, H., Ramdani, M., Kissi, M. (2020). Towards a Feature Selection for Multi-label Text Classification in Big Data. In: Hamlich, M., Bellatreche, L., Mondal, A., Ordonez, C. (eds) Smart Applications and Data Analysis. SADASC 2020. Communications in Computer and Information Science, vol 1207. Springer, Cham. https://doi.org/10.1007/978-3-030-45183-7_14
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