Sacrificing Overall Classification Quality to Improve Classification Accuracy of Well-Sought Classes
- Resource Type
- Conference
- Authors
- Amaral, Kevin M.; Chen, Ping; Ding, Wei; Sadasivam, Rajani
- Source
- 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) ICDMW Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on. :1179-1182 Dec, 2016
- Subject
- Computing and Processing
General Topics for Engineers
Testing
Training
Recruitment
Neural networks
Peer-to-peer computing
Labeling
Logistics
- Language
- ISSN
- 2375-9259
Classification has been an active field in machine learning for decades. With many methods proposed for various topics in classification, this paper intends to show some initial ideas and findings in one classification scenario where accuracy of only one or a few classes is greatly valued, while the other classes are not important. Using a neural network model and challenging real world dataset, our preliminary results showed the accuracy of important class was significantly improved by sacrificing the accuracy of unimportant classes.