Apply Genetic Algorithms to Discretization
碩士 === 國立中央大學 === 資訊管理研究所 === 93 === Discretization of continuous attributes is one of main problems needed to be solved in data mining. Discretization can be viewed as the problem of selecting a set of cut points of attributes. Past studies concentrated on finding a minimal set of cut points and ma...
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| Format: | Others |
| Language: | en_US |
| Published: |
2005
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| Online Access: | http://ndltd.ncl.edu.tw/handle/92907504347874525955 |
| Summary: | 碩士 === 國立中央大學 === 資訊管理研究所 === 93 === Discretization of continuous attributes is one of main problems needed to be solved in data
mining. Discretization can be viewed as the problem of selecting a set of cut points of
attributes. Past studies concentrated on finding a minimal set of cut points and maintaining
the fidelity of the original data in discretization. However, maintaining too high
consistency may yield too many unnecessary rules which are not general. Generality is
an important aspect to discretization because general rules are usually useful and easy
to interpret. In this paper, a genetic algorithm based approach is proposed and the aim
is to efficiently find an optimal compromise solution of discretization between generality
and consistency criterions. Two sets of experiments on some data sets from UCI Machine
Learning Repository by this approach were done. The empirical results have demonstrated
that our GA approach can generate the simplest discretization result according to the requirement of the decision maker and help the classifier to induce general rules with high
predictive accuracy.
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