creators_name: Iqbal, Ridwan Al
creators_id: stopofeger@yahoo.com
type: preprint
datestamp: 2011-02-16 19:49:38
lastmod: 2011-03-11 08:57:50
metadata_visibility: show
title: A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation
subjects: comp-sci-art-intel
subjects: comp-sci-mach-learn
full_text_status: public
keywords: Rule based learning, hybrid learning, virtual sample, virtual example, artificial sample,artificial example,pruning dataset
abstract: 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.  
date: 2011-01-25
date_type: completed
refereed: FALSE
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citation:   Iqbal, Ridwan Al  (2011) A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation.  [Preprint]     
document_url: http://cogprints.org/7180/1/RASCAL.pdf