A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning.

[1]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[2]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[3]  J. Jeffrey Mahoney and Raymond J. Mooney,et al.  Combining Symbolic and Neural Learning to Revise Probabilistic Theories , 1992 .

[4]  Michael J. Pazzani,et al.  A Knowledge-intensive Approach to Learning Relational Concepts , 1991, ML.

[5]  Gary M. Scott.  Knowledge-based artificial neural networks for process modelling and control , 1993 .

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  Michael J. Pazzani,et al.  Comprehensible Knowledge-Discovery in Databases , 1997 .

[8]  Ting Yu.  Incorporating prior domain knowledge into inductive machine learning : its implementation in contemporary capital markets , 2007 .

[9]  Simon Haykin,et al.  Incorporating Prior Information in Machine Learning by Creating Virtual Examples , 2001 .

[11]  Manabu Sassano,et al.  Virtual Examples for Text Classification with Support Vector Machines , 2003, EMNLP.

[12]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[13]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[14]  Tom M. Mitchell,et al.  Concept Learning and the General-to-Specific Ordering , 1997 .

[15]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[16]  Bernhard Schölkopf,et al.  Incorporating Invariances in Support Vector Learning Machines , 1996, ICANN.

[17]  Tomaso Poggio,et al.  Incorporating prior information in machine learning by creating virtual examples , 1998, Proc. IEEE.