Name
Abstract | ||
|---|---|---|
There is a chicken-and-egg problem in classification whereby a good classifier is required to test the efficacy of the features, yet a good feature set is required to generate a good classifier. When the salient features are unknown, an extremely large set of features is used to train the classifier in hopes of obtaining accurate classification results. This research proposes the use of a special class of decision tree called the alternating decision tree or ADTree to answer two questions in knowledge discovery in order to effectively select a salient feature set: When using a particular feature extraction algorithm, which of the features is able to distinguish between the different classes? And how do they work? |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/I2MTC.2013.6555573 | Instrumentation and Measurement Technology Conference |
Keywords | Field | DocType |
data mining,feature extraction,image classification,tree data structures,adtree,chicken-and-egg problem,feature reduction,feature set,knowledge discovery,hog,svm,boosting,decision trees,classification algorithms,accuracy,support vector machines | Decision tree,Pattern recognition,Feature (computer vision),Computer science,Tree (data structure),Feature extraction,Knowledge extraction,Artificial intelligence,Classifier (linguistics),Linear classifier,Alternating decision tree,Machine learning | Conference |
ISSN | ISBN | Citations |
1091-5281 | 978-1-4673-4621-4 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Kuan Sok | 1 | 0 | 0.34 |
| Chowdhury, M.S. | 2 | 4 | 0.90 |
| Ooi, M.P.-L. | 3 | 0 | 0.34 |
| Demidenko, S. | 4 | 30 | 11.55 |