NDLI: BRACID: a comprehensive approach to learning rules from imbalanced data

Content Provider	Springer Nature Link
Author	Napierala, Krystyna Stefawski, Jerzy
Copyright Year	2011
Abstract	In this paper we consider induction of rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining majority classes. The minority class is usually of primary interest. However, most rule-based classifiers are biased towards the majority classes and they have difficulties with correct recognition of the minority class. In this paper we discuss sources of these difficulties related to data characteristics or to an algorithm itself. Among the problems related to the data distribution we focus on the role of small disjuncts, overlapping of classes and presence of noisy examples. Then, we show that standard techniques for induction of rule-based classifiers, such as sequential covering, top-down induction of rules or classification strategies, were created with the assumption of balanced data distribution, and we explain why they are biased towards the majority classes. Some modifications of rule-based classifiers have been already introduced, but they usually concentrate on individual problems. Therefore, we propose a novel algorithm, BRACID, which more comprehensively addresses the issues associated with imbalanced data. Its main characteristics includes a hybrid representation of rules and single examples, bottom-up learning of rules and a local classification strategy using nearest rules. The usefulness of BRACID has been evaluated in experiments on several imbalanced datasets. The results show that BRACID significantly outperforms the well known rule-based classifiers C4.5rules, RIPPER, PART, CN2, MODLEM as well as other related classifiers as RISE or K-NN. Moreover, it is comparable or better than the studied approaches specialized for imbalanced data such as generalizations of rule algorithms or combinations of SMOTE + ENN preprocessing with PART. Finally, it improves the support of minority class rules, leading to better recognition of the minority class examples.
Starting Page	335
Ending Page	373
Page Count	39
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	39
Issue Number	2
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2011-12-30
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rule induction Imbalanced data Classifiers Nearest neighbour paradigm Nearest rules Artificial Intelligence (incl. Robotics) Business Information Systems Information Storage and Retrieval Document Preparation and Text Processing Data Structures, Cryptology and Information Theory
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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

BRACID: a comprehensive approach to learning rules from imbalanced data

Types of minority class examples and their influence on learning classifiers from imbalanced data

BruteSuppression: a size reduction method for Apriori rule sets

Hierarchical object-driven action rules

Bootstrapping rule induction to achieve rule stability and reduction

A rule-based expert system for earthquake prediction

Efficient Rule-Based Attribute-Oriented Induction for Data Mining

Mining complex patterns

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

BRACID: a comprehensive approach to learning rules from imbalanced data

Similar Documents

BRACID: a comprehensive approach to learning rules from imbalanced data

Types of minority class examples and their influence on learning classifiers from imbalanced data

BruteSuppression: a size reduction method for Apriori rule sets

Hierarchical object-driven action rules

Bootstrapping rule induction to achieve rule stability and reduction

A rule-based expert system for earthquake prediction

Efficient Rule-Based Attribute-Oriented Induction for Data Mining

Mining complex patterns

DART+: Direction-aware bichromatic reverse k nearest neighbor query processing in spatial databases

BRACID: a comprehensive approach to learning rules from imbalanced data