Name
Abstract | ||
|---|---|---|
Complex number features are ubiquitous in many engineering and scientific applications. Many traditional classification algorithms including alternating decision tree (ADTree) are very powerful but not capable of handling complex domain data. ADTree is classifier that is intrinsically support boosting, hence inherent all desirable statistical properties of boosting methodology. This work introduces base learners that enable application of ADTree algorithm to complex domain data. The presented results show that the proposed base learners enhance performance of ADTrees on complex domain features. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1504/IJISTA.2010.036587 | IJISTA |
Keywords | Field | DocType |
adtree algorithm,scientific application,intrinsically support,desirable statistical property,complex feature,decision tree,complex number feature,proposed base learner,complex domain feature,base learner,complex domain data,supervised learning,boosting,decision trees,alternating decision tree | Decision tree,Computer science,Supervised learning,Boosting (machine learning),Artificial intelligence,Ubiquitous computing,Statistical classification,Classifier (linguistics),Group method of data handling,Machine learning,Alternating decision tree | Journal |
Volume | Issue | Citations |
9 | 3/4 | 4 |
PageRank | References | Authors |
0.43 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Chow Kuang | 1 | 72 | 19.81 |
| Melanie Po-Leen Ooi | 2 | 70 | 18.35 |