Name
Abstract | ||
|---|---|---|
Classification of a large document collection involves dealing with a huge feature space where each distinct word is a feature. In such an environment, classification is a costly task both in terms of running time and computing resources. Further it will not guarantee optimal results because it is likely to overfit by considering every feature for classification. In such a context, feature selection is inevitable. This work analyses the feature selection methods, explores the relations among them and attempts to find a minimal subset of features which are discriminative for document classification. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34475-6_44 | ICONIP (1) |
Keywords | DocType | Volume |
feature selection,large document collection,optimal result,distinct word,huge feature space,minimal subset,feature selection method,discriminative feature analysis,costly task,document classification,computing resource,scalability,classification | Conference | 7663 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.36 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Punya Murthy Chinta | 1 | 1 | 0.36 |
| M. Narasimha Murty | 2 | 824 | 86.07 |