NDLI: A New Performance Measure for Class Imbalance Learning. Application to Bioinformatics Problems

Content Provider	IEEE Xplore Digital Library
Author	Batuwita, R. Palade, V.
Copyright Year	2009
Abstract	In class imbalance learning, the performance measure used for the model selection would play a vital role. It has been well-studied in the past research that the most widely used performance measure, the overall accuracy of the model, can lead to sub-optimal classification models when learning from imbalanced datasets. In order to overcome this problem, other performance measures, such as the Geometric-mean (Gm) and F-measure (Fm), have been used for imbalanced dataset learning. Training a classifier system with an imbalanced dataset (where the positive class is the minority class) would usually produce sub-optimal models having a higher Specificity (SP) and a lower Sensitivity (SE). By applying class imbalance learning methods, we would often be able to increase the SE by sacrificing some amount of SP. In some type of real world imbalanced classification problems, such as the gene finding Bioinformatics problems, it is important to improve the SE as much as possible by keeping the reduction of SP to the minimum. In this paper, we show that with respect to this type of classification problems the existing performance measures used in class imbalance learning (Gm and Fm) can still result in sub-optimal classification models. In order to circumvent these problems, we introduced a new performance measure, called Adjusted Geometric-mean (AGm). We show, both analytically and empirically on two real-world Bioinformatics datasets, that AGm can perform better than Gm and Fm metrics.
Starting Page	545
Ending Page	550
File Size	414957
Page Count	6
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Costs Laboratories Predictive models Data processing Electronic mail Performance Measures SVMs Learning systems Proteins Machine learning Model Selection Performance analysis Bioinformatics Class Imbalance Learning
Content Type	Text
Resource Type	Article

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

EUS SVMs: Ensemble of UnderSampled SVMs for Data Imbalance Problems

Exploratory Undersampling for Class-Imbalance Learning

Evaluating Model Selection Abilities of Performance Measures (2006)

Addressing Class Imbalance in Non-binary Classification Problems

Model selection of SVMs using GA approach

Evaluation of imbalanced datasets using fuzzy support vector machine-class imbalance learning (FSVM-CIL)

Model selection for multi-class SVMs (2005)

A New Geometric Approach to the Complexity of Model Selection

Combating the Small Sample Class Imbalance Problem Using Feature Selection

A New Performance Measure for Class Imbalance Learning. Application to Bioinformatics Problems

Similar Documents

EUS SVMs: Ensemble of UnderSampled SVMs for Data Imbalance Problems

Exploratory Undersampling for Class-Imbalance Learning

Evaluating Model Selection Abilities of Performance Measures (2006)

Addressing Class Imbalance in Non-binary Classification Problems

Model selection of SVMs using GA approach

Evaluation of imbalanced datasets using fuzzy support vector machine-class imbalance learning (FSVM-CIL)

Model selection for multi-class SVMs (2005)

A New Geometric Approach to the Complexity of Model Selection

Combating the Small Sample Class Imbalance Problem Using Feature Selection

A New Performance Measure for Class Imbalance Learning. Application to Bioinformatics Problems