Name
Abstract | ||
|---|---|---|
The paper presents a cost-sensitive modification of the Adaptive Splitting and Selection (AdaSS) algorithm, which trains a combined classifier based on a feature space partitioning. In this study the algorithm considers constraints put on the cost of selected features, which are one of the key-problems in the clinical decision support systems. The modified version takes into consideration both the overall classification accuracy and the cost constraints, returning balanced solution for the problem at hand. Proposed method was evaluated on the basis of computer experiments run on cost-sensitive medical benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-32639-4_101 | IDEAL |
Keywords | Field | DocType |
adaptive splitting,balanced solution,feature space partitioning,clinical decision support system,medical decision support system,cost-sensitive modification,cost constraint,selection method,cost-sensitive medical benchmark datasets,combined classifier,cost-sensitive splitting,computer experiment,machine learning,evolutionary algorithm,feature selection | Data mining,Feature selection,Evolutionary algorithm,Computer science,Artificial intelligence,Clinical decision support system,Classifier (linguistics),Computer experiment,Feature vector,Pattern recognition,Decision support system,Train,Machine learning | Conference |
Citations | PageRank | References |
9 | 0.56 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konrad Jackowski | 1 | 136 | 10.46 |
| Bartosz Krawczyk | 2 | 721 | 60.97 |
| Michał Woźniak | 3 | 213 | 24.64 |