Genetic Algorithm and Ensemble Learning Aided Text Classification using Support Vector Machines

Text classification is one of the areas where machine learning algorithms are used. The size of the dataset and the methods used for converting the textual words into vectors play a major role in classifying them. This paper proposes a heuristic based approach to classify the documents using Genetic Algorithm aided Support Vector Machines (SVM) and Ensemble Learning approach. The real valued representation of the textual data into vectors is done on applying Term Frequency – Inverse Document Frequency (TF-IDF) and Bi-Normal Separation (BNS). However, in this paper, the common data misclassification issue in SVM is overcome by introducing two algorithms that adds weightage to accurate classification. The first algorithm applied BNS and TF-IDF along with ensemble learning and constructs a voting classifier for classifying the textual documents. The results produced justify that TF-IDF produces good results with voting classifier than BNS for classification. Henceforth TF-IDF is applied in the subsequent approach for vector generation. Secondly, genetic algorithm is applied along with OneVsRest strategy in SVM to overcome the drawback of multiclass multilabel classification. The results show that Genetic algorithm improves the accuracy of classification even with a very small labelled dataset, as genetic algorithm applies the process of Mutation and Cross over across many generations to understand the pattern of right classification.

@article{Chauhan2021,
  title     = {Genetic Algorithm and Ensemble Learning Aided Text Classification using Support Vector Machines},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {8},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Anshumaan Chauhan and Ayushi Agarwal and Razia Sulthana},
  doi       = {10.14569/IJACSA.2021.0120830},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120830}
}