Name
Abstract | ||
|---|---|---|
The main advantage of tree classifiers is to provide rules that are simple in form and are easily interpretable. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining the splitting variables and their thresholds for a decision tree using an adaptive particle swarm optimization. The proposed method consists of three phases - tree construction, threshold optimization and rule simplification. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed method is promising for improving prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-40361-3_2 | ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: COMPETITIVE MANUFACTURING FOR INNOVATIVE PRODUCTS AND SERVICES, AMPS 2012, PT II |
Keywords | Field | DocType |
Classification,Data mining,Decision tree,Particle swarm optimization | Particle swarm optimization,Decision tree,Data mining,Computer science,Multi-swarm optimization,Artificial intelligence,Divide and conquer algorithms,Machine learning,Incremental decision tree | Conference |
Volume | ISSN | Citations |
398 | 1868-4238 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chi-hyuck Jun | 1 | 527 | 48.18 |
| Yun-Ju Cho | 2 | 0 | 0.34 |
| Hyeseon Lee | 3 | 28 | 3.75 |