Name
Abstract | ||
|---|---|---|
A framework of cost-sensitive classification based on decision-theoretic rough set model is proposed to determine the local minimum total cost classification and the local optimal test attributes set. Based on the proposed classification strategy, a cost-sensitive classification algorithm CSDTRS is presented. CSDTRS focuses on searching for an optimal test attributes set with minimum total cost including both misclassification cost and test cost, and then determine the classification based on the optimal test attributes set. A heuristic function for evaluating the attribute is presented to determine which attribute should be added in the optimal test attributes set. Experiments on four UCI data sets are performed to show the effectiveness of the proposed classification algorithm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31900-6_47 | RSKT |
Keywords | Field | DocType |
uci data set,cost-sensitive classification algorithm,cost-sensitive classification,optimal test,local optimal test,test cost,proposed classification algorithm,proposed classification strategy,local minimum total cost,decision-theoretic rough set model,attribute selection | Heuristic function,Data mining,Data set,Pattern recognition,Feature selection,Computer science,Rough set,Artificial intelligence,Total cost,Machine learning | Conference |
Citations | PageRank | References |
19 | 0.68 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaxiong Li | 1 | 770 | 35.51 |
| Xianzhong Zhou | 2 | 439 | 27.01 |
| Jiabao Zhao | 3 | 114 | 3.94 |
| Bing Huang | 4 | 471 | 21.34 |