Performance Comparison of Machine Learning Algorithms for Albanian News articles
Lamir Shkurti, Faton Kabashi, Vehebi Sofiu, Arsim Susuri · IFAC-PapersOnLine · 2022
This paper presented a methodology to classify news articles in Albanian languages with machine learning algorithms and NLP. In this proposed work Multinomial Naïve Bayes, Logistic Regression, k-nearest neighbors, Bernoulli, Centroid, SVM, SGD, Perceptron, Passive Aggressive, Decision Tree, and Random Forest machine learning Classification algorithms are trained to classify Albanian news articles and compared in term of accuracy, training time and testing time. We have used 70% of the dataset for training and 30% for tests. The execution time of training and testing algorithms is recorded and presented for different input sizes of data. For experimental we used different numbers of inputs, starting from 8000 articles, and continuing in increasing order by 8000 until the level of 80000 in 8 categories. Experimental results indicate that the Passive Aggressive algorithm shows the best performance in terms of accuracy for Albanian news articles. While the classifier with the lowest accuracy was Random Forest. Logistic Regression, SVM, and Decision Tree have a high delay in training data. SVM also has the longest testing time compared to other classifiers.