Türkçe haber portallarında metin sınıflandırma ve topluluk budama

In news portals, text category information is needed for news presentation. However,for many news stories the category information is unavailable, incorrectlyassigned or too generic. This makes the text categorization a necessary toolfor news portals. Automated text categorization (ATC) is a multifaceted difficultprocess that involves decisions regarding tuning of several parameters, termweighting, word stemming, word stopping, and feature selection. It is importantto find a categorization setup that will provide highly accurate results in ATC forTurkish news portals. Two Turkish test collections with different characteristicsare created using Bilkent News Portal. Experiments are conducted with four classificationmethods: C4.5, KNN, Naive Bayes, and SVM (using polynomial andrbf kernels). Results recommend a text categorization template for Turkish newsportals. Regarding recommended text categorization template, ensemble learningmethods are applied to increase effectiveness. Since they require many computationalworkload, ensemble pruning strategies are developed. Data partitioningensembles are constructed and ranked-based ensemble pruning is applied withseveral machine learning categorization algorithms. The aim is to answer the followingquestions: (1) How much data can we prune using data partitioning on thetext categorization domain? (2) Which partitioning and categorization methodsare more suitable for ensemble pruning? (3) How do English and Turkish differin ensemble pruning? (4) Can we increase effectiveness with ensemble pruningin the text categorization? Experiments are conducted on two text collections:Reuters-21578 and BilCat-TRT. 90% of ensemble members can be pruned withalmost no decreasing in accuracy.