Combining ILP with Semi-supervised Learning for Web Page Categorization

This paper presents a semi-supervised learning algorithm called Iterative-Cross Training (ICT) to solve the Web pages classification problems. We apply Inductive logic programming (ILP) as a strong learner in ICT. The objective of this research is to evaluate the potential of the strong learner in order to boost the performance of the weak learner of ICT. We compare the result with the supervised Naive Bayes, which is the well-known algorithm for the text classification problem. The performance of our learning algorithm is also compare with other semi-supervised learning algorithms which are Co-Training and EM. The experimental results show that ICT algorithm outperforms those algorithms and the performance of the weak learner can be enhanced by ILP system. Keywords--Inductive Logic Programming, Semi-supervised Learning, Web Page Categorization.

• Type: Conference
• Year: 2004
• Where: IJIT
• Authors: Nuanwan Soonthornphisaj, Boonserm Kijsirikul
• Keywords: Artificial Intelligence, IJIT 2004, Semi-supervised Learning Algorithms, Strong Learner, Weak Learner