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Rule Based Classification: Decision Table Majority

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Im trying to write this rule based classifier based on Ron Kohavi's Paper: The Power of Decision Table.

But the paper is very confusing and i cant follow what steps is involved in order to classify correctly... if anyone knows how please help thanks!

This is a low-level suggestion that reveals more about my ignorance than my knowledge, but is part of the issue that you are attempting to create a complete system, whereas Kohavi deals with only half a system. His words:

To build a DTM, the induction algorithm must decide which features to include in the schema and which instances to store in the body. In this paper we restrict ourselves to the former problem, called feature subset selection.

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So this isn't the paper which explains the process of classification using a decision table? but i cant find any other paper related to this subject..

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