NDLI: Visualizing Classifier Performance on Different Domains

Content Provider	IEEE Xplore Digital Library
Author	Alaiz-Rodriguez, R. Japkowicz, N. Tischer, P.
Copyright Year	2008
Description	Author affiliation: Dipt. de Ing. Electr. y de Sist., Univ. de Leon, Leon (Alaiz-Rodriguez, R.) \|\| Sch. of Inf. Technol. & Eng., Univ. of Ottawa, Ottawa, ON (Japkowicz, N.) \|\| Clayton Sch. of Inf. Technol., Monash Univ., Clayton, VIC (Tischer, P.)
Abstract	Classifier performance evaluation typically gives rise to vast numbers of results that are difficult to interpret. On the one hand, a variety of different performance metrics can be applied; and on the other hand, evaluation must be conducted on multiple domains to get a clear view of the classifier's general behaviour. In this paper, we present a visualization technique that allows a user to study the results from a domain point of view and from a classifier point of view. We argue that classifier evaluation should be done on an exploratory basis. In particular, we suggest that, rather than pre-selecting a few metrics and domains to conduct our evaluation on, we should use as many metrics and domains as possible and mine the results of this study to draw valid and relevant knowledge about the behaviour of our algorithms. The technique presented in this paper will enable such a process.
Starting Page	3
Ending Page	10
File Size	318509
Page Count	8
File Format	PDF
ISBN	9780769534404
ISSN	10823409
DOI	10.1109/ICTAI.2008.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information technology Measurement Data mining Artificial intelligence Machine learning algorithms Machine learning Algorithm design and analysis Data visualization Performance analysis visualization classifier evaluation
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

PaDDMAS: parallel and distributed data mining application suite

On Machine Learning with Imbalanced Data and Research Quality Evaluation Methodologies

A Study on the Relationships of Classifier Performance Metrics

Performance analysis of ABCMiner algorithm with different objective functions

Quantitative Association Rules Mining Algorithm Based on Matrix

Visualizing corporate data

Speeding up AdaBoost Classifier with Random Projection

Adapting classification rule induction to subgroup discovery

On dynamic data clustering and visualization using swarm intelligence

Visualizing Classifier Performance on Different Domains

Similar Documents

PaDDMAS: parallel and distributed data mining application suite

On Machine Learning with Imbalanced Data and Research Quality Evaluation Methodologies

A Study on the Relationships of Classifier Performance Metrics

Performance analysis of ABCMiner algorithm with different objective functions

Quantitative Association Rules Mining Algorithm Based on Matrix

Visualizing corporate data

Speeding up AdaBoost Classifier with Random Projection

Adapting classification rule induction to subgroup discovery

On dynamic data clustering and visualization using swarm intelligence

Visualizing Classifier Performance on Different Domains