NDLI: UniDis: a universal discretization technique

Content Provider	Springer Nature Link
Author	Sang, Yu Jin, Yingwei Li, Keqiu Qi, Heng
Copyright Year	2012
Abstract	Discretization techniques have played an important role in machine learning and data mining as most methods in such areas require that the training data set contains only discrete attributes. Data discretization unification (DDU), one of the state-of-the-art discretization techniques, trades off classification errors and the number of discretized intervals, and unifies existing discretization criteria. However, it suffers from two deficiencies. First, the efficiency of DDU is very low as it conducts a large number of parameters to search good results, which does not still guarantee to obtain an optimal solution. Second, DDU does not take into account the number of inconsistent records produced by discretization, which leads to unnecessary information loss. To overcome the above deficiencies, this paper presents a Uni versal Dis cretization technique, namely UniDis. We first develop a non-parametric normalized discretization criteria which avoids the effect of relatively large difference between classification errors and the number of discretized intervals on discretization results. In addition, we define a new entropy-based measure of inconsistency for multi-dimensional variables to effectively control information loss while producing a concise summarization of continuous variables. Finally, we propose a heuristic algorithm to guarantee better discretization based on the non-parametric normalized discretization criteria and the entropy-based inconsistency. Besides theoretical analysis, experimental results demonstrate that our approach is statistically comparable to DDU evaluated by a popular statistical test and it yields a better discretization scheme which significantly improves the accuracy of classification than previously other known discretization methods except for DDU by running J4.8 decision tree and Naive Bayes classifier.
Starting Page	327
Ending Page	348
Page Count	22
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	40
Issue Number	2
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2012-11-22
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discretization Inconsistency Entropy J4.8 decision tree Naive Bayes classifier Information Storage and Retrieval Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Business Information Systems Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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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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