Abstract
| Language | English |
|---|---|
| Pages | 1257-1273 |
| Journal | IEEE Transactions on Fuzzy Systems |
| Volume | 26 |
| Issue number | 3 |
| Early online date | 27 Jun 2017 |
| DOIs | |
| Publication status | Published - 30 Jun 2018 |
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Keywords
- Attribute reduction, feature selection, fuzzy rough sets, incremental learning, relative discernibility relation
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Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets. / Yang, Yanyan; Chen, Degang; Wang, H.; Wang, Xizhao.
In: IEEE Transactions on Fuzzy Systems, Vol. 26, No. 3, 30.06.2018, p. 1257-1273.Research output: Contribution to journal › Article
TY - JOUR
T1 - Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets
AU - Yang, Yanyan
AU - Chen, Degang
AU - Wang, H.
AU - Wang, Xizhao
N1 - Exception: The lead author (Dr Yanyan Yang) was pregnant with some complications.
PY - 2018/6/30
Y1 - 2018/6/30
N2 - Feature selection based on fuzzy rough sets is an effective approach to select a compact feature subset that optimally predicts a given decision label. Despite being studied extensively, most existing methods of fuzzy rough set based feature selection are restricted to computing the whole dataset in batch, which is often costly or even intractable for large datasets. To improve the time efficiency, we investigate the incremental perspective for fuzzy rough set based feature selection assuming data can be presented in sample subsets one after another. The key challenge for the incremental perspective is how to add and delete features with the subsequent arrival of sample subsets. We tackle this challenge with strategies of adding and deleting features based on the relative discernibility relations that are updated as subsets arrive sequentially. Two incremental algorithms for fuzzy rough set based feature selection are designed based on the strategies. One updates the selected features as each sample subset arrives, and outputs the final feature subset where no sample subset is left. The other updates the relative discernibility relations but only performs feature selection where there is no further subset arriving. Experimental comparisons suggest our incremental algorithms expedite fuzzy rough set based feature selection without compromising performance.
AB - Feature selection based on fuzzy rough sets is an effective approach to select a compact feature subset that optimally predicts a given decision label. Despite being studied extensively, most existing methods of fuzzy rough set based feature selection are restricted to computing the whole dataset in batch, which is often costly or even intractable for large datasets. To improve the time efficiency, we investigate the incremental perspective for fuzzy rough set based feature selection assuming data can be presented in sample subsets one after another. The key challenge for the incremental perspective is how to add and delete features with the subsequent arrival of sample subsets. We tackle this challenge with strategies of adding and deleting features based on the relative discernibility relations that are updated as subsets arrive sequentially. Two incremental algorithms for fuzzy rough set based feature selection are designed based on the strategies. One updates the selected features as each sample subset arrives, and outputs the final feature subset where no sample subset is left. The other updates the relative discernibility relations but only performs feature selection where there is no further subset arriving. Experimental comparisons suggest our incremental algorithms expedite fuzzy rough set based feature selection without compromising performance.
KW - Attribute reduction, feature selection, fuzzy rough sets, incremental learning, relative discernibility relation
U2 - 10.1109/TFUZZ.2017.2718492
DO - 10.1109/TFUZZ.2017.2718492
M3 - Article
VL - 26
SP - 1257
EP - 1273
JO - IEEE Transactions on Fuzzy Systems
T2 - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
SN - 1063-6706
IS - 3
ER -