Name
Abstract | ||
|---|---|---|
Often in pattern classification problems, one tries to extract a large number of features and base the classifier decision on as much information as possible. This yields an array of features that are 'potentially' useful. Most of the time however, large feature sets are sub-optimal in describing the samples since they tend to over-represent the data and model noise along with the useful information in the data. Selecting relevant features from the available set of features is, therefore, a challenging task. In this paper, we present an innovative feature selection algorithm called Smart Beam Search (SBS), which is used with a support vector machine (SVM) based classifier for automatic defect classification. This feature selection approach not only reduces the dimensionality of the feature space substantially, but also improves the classifier performance. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICPR.2002.1048275 | Pattern Recognition, 2002. Proceedings. 16th International Conference |
Keywords | Field | DocType |
feature extraction,flaw detection,learning automata,pattern classification,pattern recognition,semiconductor device testing,Smart Beam Search,automatic SVM defect classification,automatic defect classification,beam search,classifier decision,dimensionality,feature extraction,feature selection algorithm,large feature sets,noise,pattern classification problems,relevant features,semiconductor industry,support vector machine | Structured support vector machine,Data mining,Feature vector,Dimensionality reduction,Feature selection,Pattern recognition,Computer science,Feature (computer vision),Feature extraction,Feature (machine learning),Artificial intelligence,Linear classifier | Conference |
Volume | ISSN | Citations |
2 | 1051-4651 | 9 |
PageRank | References | Authors |
0.59 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| p gupta | 1 | 17 | 1.23 |
| David Doermann | 2 | 4313 | 312.70 |
| Daniel Dementhon | 3 | 1327 | 139.94 |