Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?

TL;DR

This paper proposes a hybrid feature selection approach combined with fastText and neural networks to improve text classification by reducing features and training time, with some accuracy gains.

Contribution

It introduces a novel hybrid feature selection method integrated with neural networks and fastText for more efficient text classification.

Findings

Reduced training time with feature selection

Slight accuracy improvements on some datasets

Effective combination of filter methods with neural networks

Abstract

Text classification is a fundamental problem in the field of natural language processing. Text classification mainly focuses on giving more importance to all the relevant features that help classify the textual data. Apart from these, the text can have redundant or highly correlated features. These features increase the complexity of the classification algorithm. Thus, many dimensionality reduction methods were proposed with the traditional machine learning classifiers. The use of dimensionality reduction methods with machine learning classifiers has achieved good results. In this paper, we propose a hybrid feature selection method for obtaining relevant features by combining various filter-based feature selection methods and fastText classifier. We then present three ways of implementing a feature selection and neural network pipeline. We observed a reduction in training time when feature selection methods are used along with neural networks. We also observed a slight increase in accuracy on some datasets.