Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-Tuning Hyper-Parameters Approach. A Case Study on Automatic Classification of Global Terrorist Attacks

TL;DR

This paper improves SGD-based text classification by applying grid-search hyper-parameter tuning, demonstrating enhanced accuracy and efficiency in classifying global terrorist attack data across multiple classifiers.

Contribution

It introduces a grid-search hyper-parameter tuning method for SGD in text classification, validated on terrorist attack data with multiple classifiers.

Findings

Hyper-parameter tuning improves classification accuracy.

Optimized SGD reduces execution time.

Method is effective across different classifiers.

Abstract

The objective of this research is to enhance performance of Stochastic Gradient Descent (SGD) algorithm in text classification. In our research, we proposed using SGD learning with Grid-Search approach to fine-tuning hyper-parameters in order to enhance the performance of SGD classification. We explored different settings for representation, transformation and weighting features from the summary description of terrorist attacks incidents obtained from the Global Terrorism Database as a pre-classification step, and validated SGD learning on Support Vector Machine (SVM), Logistic Regression and Perceptron classifiers by stratified 10-K-fold cross-validation to compare the performance of different classifiers embedded in SGD algorithm. The research concludes that using a grid-search to find the hyper-parameters optimize SGD classification, not in the pre-classification settings only, but also in the performance of the classifiers in terms of accuracy and execution time.