Name
Abstract | ||
|---|---|---|
The importance of cost-sensitive learning becomes crucial when the costs of misclassifications are quite different. Many evidences have demonstrated that a cost-sensitive predictive model is more desirable in practical applications than a traditional one without taking the cost into consideration. In this paper, we propose two approaches which incorporate the cost matrix into original learning vector quantization by means of instance weighting. Empirical results show that the proposed algorithms are effective on both binary-class data and multi-class data. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15825-4_33 | ICANN (3) |
Keywords | Field | DocType |
cost-sensitive learning,empirical result,binary-class data,practical application,original learning vector quantization,weighted learning vector quantization,multi-class data,proposed algorithm,cost-sensitive predictive model,instance weighting,cost matrix,classification,prediction model,neural network,learning vector quantization | Data mining,Competitive learning,Semi-supervised learning,Instance-based learning,Active learning (machine learning),Computer science,Vector quantization,Artificial intelligence,Computational learning theory,Online machine learning,Pattern recognition,Learning vector quantization,Machine learning | Conference |
Volume | ISSN | ISBN |
6354 | 0302-9743 | 3-642-15824-2 |
Citations | PageRank | References |
4 | 0.42 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Chen | 1 | 166 | 15.49 |
| Bernardete Ribeiro | 2 | 758 | 82.07 |
| Armando Vieira | 3 | 147 | 11.48 |
| João Duarte | 4 | 67 | 5.10 |
| João C. Neves | 5 | 46 | 3.07 |