  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.
