Name
Abstract | ||
|---|---|---|
Feature selection offers a crucial way to reduce the irrelevant and misleading features for a given problem, while retaining the underlying semantics of selected features. Whilst maintaining the quality of problem-solving (e.g., classification), a superior feature selection process should be reduce the number of attributes as much as possible. In this paper, a non-unique decision value (NDV), which is defined as the number of attribute values that can lead to non-unique decision values, is proposed to rapidly capture the uncertainty in the boundary region of a granular space. Also, as an evaluator of the selected feature subset, an NDV-based differentiation entropy (NDE) is introduced to implement a novel feature selection process. The experimental results demonstrate that the selected features by the proposed approach outperform those attained by other state-of-the-art feature selection methods, in respect of both the size of reduction and the classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/FSKD.2017.8392934 | 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) |
Keywords | Field | DocType |
selected feature subset,state-of-the-art feature selection methods,rough set boundary region,irrelevant features,misleading features,problem-solving,superior feature selection process,nonunique decision value,attribute values,NDV-based differentiation entropy,NDE | Pattern recognition,Feature selection,Computer science,Measurement uncertainty,Feature extraction,Rough set,Artificial intelligence,Semantics,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-2166-0 | 0 | 0.34 |
References | Authors | |
15 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Li | 1 | 31 | 17.93 |
| Yanpeng Qu | 2 | 29 | 7.46 |
| Ansheng Deng | 3 | 2 | 3.72 |
| Qiang Shen | 4 | 864 | 55.09 |
| Changjing Shang | 5 | 212 | 34.92 |