Knowledge Discovery via Incremental Learning

Knowledge Discovery techniques seek to find new information about a domain. These techniques can either be manually performed by an expert, or automated using software algorithms (Machine Learning). However some domains (such as the field of lung function testing) contain volumes of data too vast fo...

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Bibliographic Details
Main Authors: Ling, T., Johns, D.P., Byeong Ho Kang, Walls, J., Gil-Cheol Park
Format: Conference Proceeding
Language:English
Subjects:
Data analysis
Data mining
Humans
Information analysis
Knowledge acquisition
Lungs
Machine learning
Machine learning algorithms
Performance evaluation
Testing
Online Access:Request full text
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Summary:Knowledge Discovery techniques seek to find new information about a domain. These techniques can either be manually performed by an expert, or automated using software algorithms (Machine Learning). However some domains (such as the field of lung function testing) contain volumes of data too vast for effective manual analysis, and require background knowledge too complex for Machine Learning algorithms. This study examines how the Multiple Classification Ripple-Down Rules (MCRDR) Knowledge Acquisition process can be adapted to develop a new Knowledge Discovery method, Exposed MCRDR. A prototype system was developed and tested in the domain of lung function. Preliminary results suggest that the EMCRDR method can be successfully applied to efficiently discover new knowledge in a complex domain. The study also reveals many potential areas of study and development for the MCRDR method, and Knowledge Acquisition and Knowledge Discovery methods in general.
DOI:10.1109/FBIT.2007.147