Feature Partitioning for the Co-Traning Setting

Supervised learning algorithms rely on availability of labeled data. Labeled data is either scarce or involves substantial human effort in the labeling process. These two factors, along with the abundance of unlabeled data, have spurred research initiatives that exploit unlabeled data to boost supervised learning. This genre of learning algorithms that utilize unlabeled data alongside a small set of labeled data are known as semi-supervised learning algorithms. Data characteristics, such as the presence of a generative model, provide the foundation for applying these learning algorithms. Co-training is one such al gorithm that leverages existence of two redundant "views" for a data instance. Based on these two views, the co-training algorithm trains two classifiers using the labeled data. The small set of labeled data results in a pair of weak classi fiers. With the help of the unlabeled data the two classifiers alternately boost each other to achieve a high-accuracy classifier. The conditions imposed by the co-training algorithm regarding the data characteristics restrict its application to data that possesses a natural split of the feature set. In this thesis we study the co-training setting and propose to overcome the above mentioned constraint by "manufacturing" feature splits. We pose and investigate the following questions: 1 . Can a feature split be constructed for a dataset such that the co-training algorithm can be applied to it? 2. If a feature split can be engineered, would splitting the features into more than two partitions give a better classifier? In essence, does moving from co-training (2 classifiers) to k-training (k-classifiers) help? 3. Is there an optimal number of "views" for a dataset such that k-training leads to an optimal classifier? The task of obtaining feature splits is approached by modeling the problem as a graph partitioning problem. Experiments are conducted on a breadth of text datasets. Results of k-training using constructed feature sets are compared with that of the expectation-maximization algorithm, which has been successful in a semi-supervised setting. Acknowledgments I would like to thank my committee members for giving me a rich Masters thesis experience. Without their efforts this thesis would not be as it stands today. I would like to thank my advisor, Dr. Ankur Teredesai, for the constant encouragement and the positive attitude he infused during this thesis. He always had new ideas and suggestions regarding the applications of our work to various areas. As a graduate student at RIT, the Laboratory for Applied Computing (LAC) provided the ideal setting for undertaking research. I am thankful to Dr. Gaborski for nurturing this environment at LAC. This wonderful environment at LAC motivated us to put in the extra hours. I am very thankful to Dr. Khalid Al-Kofahi for taking the time from his busy schedule to be a part of my committee. His feedback was invaluable. I would also like to thank Catalin Tomai for his interest in my thesis work and for all the meetings I had with him. The discussions with him have greatly contributed to my thesis. Finally, I would like to thank each and every member of the LAC for being a part of my thesis, by providing suggestions, comments and encouragement or by just being there to listen to me.