NDLI: A New K-Ary Crisp Decision Tree Induction with Continuous Valued Attributes

Content Provider	IET Digital Library
Author	Song, Yan Yao, Shuang Yu, Donghua Shen, Yan Hu, Yuzhen
Abstract	The simplicity and interpretability of decision tree induction makes it one of the more widely used machine learning methods for data classification. However, for continuous valued (real and integer) attribute data, there is room for further improvement in classification accuracy, complexity, and tree scale. We propose a new K-ary partition discretization method with no more than K−1 cut points based on Gaussian membership functions and the expected class number. A new K-ary crisp decision tree induction is also proposed for continuous valued attributes with a Gini index, combining the proposed discretization method. Experimental results and non-parametric statistical tests on 19 real-world datasets showed that the proposed algorithm outperforms four conventional approaches in terms of both classification accuracy, tree scale, and particularly tree depth. Considering the number of nodes, the proposed methods decision tree tends to be more balanced than in the other four methods. The complexity of the proposed algorithm was relatively low.
Starting Page	999
Ending Page	1007
Page Count	9
ISSN	10224653
Volume Number	26
e-ISSN	20755597
Issue Number	Issue 5, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/26/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2017.07.015
Journal	Chinese Journal of Electronics
Publisher Date	2017-09-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Combinatorial Mathematics Continuous Valued Attributes Data Classification Data Handling Data Handling Technique Decision Tree Gaussian Membership Function Gaussian Processes Gini Index K-1 Cut Point K-ary Crisp Decision Tree Induction K-ary Partition Discretization Method Knowledge Engineering Technique Learning in AI Machine Learning Method Pattern Classification Statistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

On the handling of continuous-valued attributes in decision tree generation

On the Handling of Continuous-Valued Attributes in Decision Tree Generation

Big data decision tree for continuous-valued attributes based on unbalanced cut points

Segment Based Decision Tree Induction With Continuous Valued Attributes

Cancers classification based on deep neural networks and emotional learning approach

Robust Clustering with Topological Graph Partition

Constructing X-of-N Attributes for Decision Tree Learning

EDISC: A Class-Tailored Discretization Technique for Rule-Based Classification

Combining and comparing various machine-learning algorithms to improve dissolved gas analysis interpretation

A New K-Ary Crisp Decision Tree Induction with Continuous Valued Attributes

Similar Documents

On the handling of continuous-valued attributes in decision tree generation

On the Handling of Continuous-Valued Attributes in Decision Tree Generation

Big data decision tree for continuous-valued attributes based on unbalanced cut points

Segment Based Decision Tree Induction With Continuous Valued Attributes

Cancers classification based on deep neural networks and emotional learning approach

Robust Clustering with Topological Graph Partition

Constructing X-of-N Attributes for Decision Tree Learning

EDISC: A Class-Tailored Discretization Technique for Rule-Based Classification

Combining and comparing various machine-learning algorithms to improve dissolved gas analysis interpretation

A New K-Ary Crisp Decision Tree Induction with Continuous Valued Attributes