A Review of Inductive Learning Algorithms

Abstract

I n recent years, there has been a growing amount of research on inductive learning. Out of this research a number of promising algorithms have surfaced. I n the paper knowledge acquisition, induction, inductive learning and the categories of inductive algorithms are discussed, C L S and its family, ID3 and its derivatives, AQ and its family, and recently developed R U L E S family of inductive learning algorithms, their strengths as well as weaknesses are explained and discussed respectively. Final ly the applications of inductive learning are overviewed. 1. K n o v v l e d g e A c q u i s i t i o n Knowledge-based expert sys tems consist of two m a i n components: a knowledge b a s e a n d a n inference m e c h a n i s m . Col lect ing knowledge to form the knovvledge base is the m a i n t a s k i n the process of bui ld ing a n expert systemf 1,2,3]. T h e process of a c q u i r i n g knovvledge through i n t e r a c t i o n vvith a n expert consis ts of a prolonged series of intense , sys temat ic intervievvs, u s u a l l y extending ö v e r a long period [41. H u m a n experts are capable of u s i n g t h e i r knovvledge i n t h e i r da i ly work , but they u s u a l l y cannot s u m m a r i s e a n d general i se the i r knovvledge expl ic i t ly i n a form w h i c h i s s u f f i c i e n t l y s y s t e m a t i c , c o r r e c t a n d comple te for m a c h i n e r e p r e s e n t a t i o n a n d a p p l i c a t i o n [1], E x p e r t s y s t e m s r e q u i r e l a r g e amounts of knovvledge to achieve h i g h levels of performance, yet the

Cite this paper

@inproceedings{Makale2012ARO, title={A Review of Inductive Learning Algorithms}, author={M. T. Makale}, year={2012} }