NDLI: Discretization Based on Positive Domain and Information Entropy

Content Provider	IEEE Xplore Digital Library
Author	Zhao Juan Wang Mingchun Liu Kun Wang Can
Copyright Year	2011
Abstract	A proper discretization 1 of numerical attributes is of paramount importance on applications of data mining and machine learning. In the classical discretization algorithm based on information entropy, the importance of the breakpoints is measured by the decrement of the uncertainty level in a decision table. In this paper, a novel discretization algorithm based on positive domain is proposed. It concerns the increment of the certainty degree to measure the importance of the breakpoints for a decision table. Afterwards, by simultaneously increasing the certainty degree and decreasing the uncertainty level for a decision table, another new aggregated algorithm measures the importance of breakpoints in a comprehensive and reasonable way. Finally, the experimental result shows that the aggregated algorithm outperforms the other two algorithms in terms of both the breakpoint amount and classification accuracy on five data sets.
Starting Page	258
Ending Page	262
File Size	230743
Page Count	5
File Format	PDF
ISBN	9781457720086
DOI	10.1109/CIS.2011.65
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis information entropy Machine learning algorithms Uncertainty Accuracy positive domain Clustering algorithms Educational institutions continuous attributes Information entropy discretization
Content Type	Text
Resource Type	Article

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