Multi-Pass Instance Based Learning
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1993-03-01T00:00:00Z
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Abstract
This paper introduces a new modified approach to the instance based learning theory. Instance based learning is augmented by neighborhood spheres and multi-pass training to improve both on generalization capabilities and storage requirements. Two models for creating neighborhood spheres are investigated and put in perspective with the IBL instance based learner. The IBL system considered here is based on the proximity algorithm, the growth (additive) algorithm and a noise resistant modification of the growth additive) algorithm. The herein described experiments will address the similarity of the MPIL and the IBL algorithms, but also point out significant differences in the approach of reducing storage requirements and increasing generalization.
A time complexity analysis of the proposed multi-pass instance based learning approach is provided. Several domains are used in this study, which include a real world domain in CMOS wafer fault diagnosis to allow for a comparison of these two approaches. Finally, the task of knowledge extraction in form of rules is addressed.