Name
Title | ||
|---|---|---|
Redundant feature elimination by using approximate Markov blanket based on discriminative contribution |
Abstract | ||
|---|---|---|
As a high dimensional problem, it is a hard task to analyze the text data sets, where many weakly relevant but redundant features hurt generalization performance of classifiers. There are previous works to handle this problem by using pair-wise feature similarities, which do not consider discriminative contribution of each feature by utilizing the label information. Here we define an Approximate Markov Blanket (AMB) based on the metric of DIScriminative Contribution (DISC) to eliminate redundant features and propose the AMB-DISC algorithm. Experimental results on the data set of Reuter-21578 show AMBDISC is much better than the previous state-of-arts feature selection algorithms considering feature redundancy in terms of MicroavgF1 and MacroavgF1. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23982-3_18 | WISM (2) |
Keywords | Field | DocType |
redundant feature,pair-wise feature similarity,discriminative contribution,high dimensional problem,amb-disc algorithm,previous state-of-arts,approximate markov,text data set,previous work,feature redundancy,redundant feature elimination,approximate markov blanket | Data mining,Data set,High dimensional problem,Feature selection,Pattern recognition,Feature (computer vision),Computer science,Redundancy (engineering),Markov blanket,Artificial intelligence,Discriminative model | Conference |
Volume | ISSN | Citations |
6988 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-qiang Zeng | 1 | 76 | 7.91 |
| Sufen Chen | 2 | 17 | 3.66 |
| Hua-Xing Zou | 3 | 3 | 1.16 |