NDLI: Effective supervised discretization for classification based on correlation maximization

Content Provider	IEEE Xplore Digital Library
Author	Qiusha Zhu Lin Lin Mei-Ling Shyu Shu-Ching Chen
Copyright Year	2011
Description	Author affiliation: School of Computing and Information Sciences, Florida International University, Miami, 33199, USA (Shu-Ching Chen) \|\| Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA (Qiusha Zhu; Lin Lin; Mei-Ling Shyu)
Abstract	In many real-world applications, there are features (or attributes) that are continuous or numerical in the data. However, many classification models only take nominal features as the inputs. Therefore, it is necessary to apply discretization as a pre-processing step to transform numerical data into nominal data for such models. Well-discretized data should not only characterize the original data to produce a concise summarization, but also improve the classification performance. In this paper, a novel and effective supervised discretization algorithm based on correlation maximization (CM) is proposed by using multiple correspondence analysis (MCA) which is a technique to capture the correlations between multiple variables. For each numeric feature, the correlation information generated from MCA is used to build the discretization algorithm that maximizes the correlations between feature intervals/items and classes. Empirical comparisons with four other commonly used discretization algorithms are conducted using six well-known classifiers. Results on five UCI datasets and five TRECVID datasets demonstrate that our proposed discretization algorithm can automatically generate a better set of features (feature intervals) by maximizing their correlations with the classes and thus improve the classification performance.
Starting Page	390
Ending Page	395
File Size	127641
Page Count	6
File Format	PDF
ISBN	9781457709647
e-ISBN	9781457709661
e-ISBN	9781457709654
DOI	10.1109/IRI.2011.6009579
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Partitioning algorithms Algorithm design and analysis Data mining Entropy Symmetric matrices Matrix decomposition Classification Supervised Discretization Multiple Correspondence Analysis (MCA)
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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