Improving web page classification with unlabeled data

There are plenty of unlabeled data in different areas and effective ways are needed to be found to use them. In order to drive the useful information from these unlabeled data, semi-supervised learning methods are used. In this thesis, two different semi-supervised learning methods are proposed, namely Incremental Parallel Training with Cross-Validation (IPT-CV) and Incremental Serial Training (IST). The proposed semi supervised learning methods employ supervised classifiers and different views of the datasets for labeling unlabeled data efficiently. Therefore, to determine which classifiers and feature extraction algorithms should be used in the proposed semi-supervised learning methods experiments are performed. Then, to evaluate the effectiveness of the proposed methods, two known semi-supervised learning methods are implemented; Co-Training, and Iterative Cross-Training (ICT). Since web is a land of unlabeled files that is increasing tremendously, the web domain is chosen for the experiments. In the thesis, 13 binary classification datasets are used from the publicly available WebKB (i.e., Course, Faculty, Project, and Student), Banksearch (i.e., Biology, Commercial Banks, Motor Sport, and Programming), SyskillWebert (i.e., Bands, Biomedical, Goats, and Sheep) datasets, as well as manually collected Conference dataset. Experiments on 30 different randomly chosen initial labeled sets are made for each dataset and the results are analyzed statistically. According to these analyses, it is observed that the performance of the two proposed methods are very high, especially the IPT-CV method has the highest classifying performance among all methods.