2012Journal of Northeast Normal UniversityRequires access

One method of classification based on discretization of continuous attributes

Yingjuan Sun

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Abstract

This paper gives a new method of classification based on discretization of continuous attributes.Firstly condition attributes are sorted in descending order by their significance,and then each condition attribute in the decision table is discretized in sequence by the order.Both discretized condition attributes and decision attributes are paid more attention during the course of discretization.And the discretized decision table needs not to be reduced further.Finally,the simulation data and the UCI machine learning data are used to verify the new method,and the new method is compared with other discretization algorithms.The results fully show the correctness and effectiveness of the proposed method of classification based on discretization of continuous attributes.

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What this paper is about

This paper gives a new method of classification based on discretization of continuous attributes.Firstly condition attributes are sorted in descending order by their significance,and then each condition attribute in the decision table is discretized in sequence by the order.Both discretized condition attributes and decision attributes are paid more attention during the course of discretization.And the discretized decision table needs not to be reduced further.Finally,the simulation data and the UCI machine learning data are used to verify the new method,and the new method is compared with other discretization algorithms.The results fully show the correctness and effectiveness of the proposed method of classification based on discretization of continuous attributes.

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Available abstract

This paper gives a new method of classification based on discretization of continuous attributes.Firstly condition attributes are sorted in descending order by their significance,and then each condition attribute in the decision table is discretized in sequence by the order.Both discretized condition attributes and decision attributes are paid more attention during the course of discretization.And the discretized decision table needs not to be reduced further.Finally,the simulation data and the UCI machine learning data are used to verify the new method,and the new method is compared with other discretization algorithms.The results fully show the correctness and effectiveness of the proposed method of classification based on discretization of continuous attributes.

Key concepts: Discretization, Discretization of continuous features, Correctness, Decision table, Sequence (biology), Computer science, Table (database), Mathematics

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