Enhanced Fuzzy Roughset based Feature Selection Strategy using Differential Evolution
| dc.category | Journal Article | |
| dc.contributor.author | Kalpana, B | |
| dc.date.accessioned | 2017-03-28T23:37:42Z | |
| dc.date.available | 2017-03-28T23:37:42Z | |
| dc.date.issued | 2013 | |
| dc.department | Computer Science | en_US |
| dc.description.abstract | Dimensionality reduction technique aims to c J ^ ^ c e the performance o f the classification model by removing the irrelevant and redfummm data. Reducing the number o f attributes in the classification model helps to alleviate^Mp curse o f dimensionality, where it is the major crisis to data storage and also facilitates the mining techniques like classification, clustering, communi^lmb^, visualization and high-dimensional data storage. Once the dimensionality o f a n g ^ J ^ increases then it leads the data to sparsity. Sparsity problem is quite tricky for e^ytechnique that entails statistical importance. Thus, it is necessary to remove the^ ir iy ^ a n t and redundant features, augment the performance o f the classifier model, speed learning task, interpret the model and highlight the vital features with their ralmidm. In this paper, a modfied feature selection technique based on fuzzy Roughset t h e ^ ^ n d Differential Evolution is proposed. Here, the experimental results are carried outtSmhg binary and multiclass datasets taken from UCI Machine Learning R ep o sitory\ | en_US |
| dc.identifier.uri | https://ir.avinuty.ac.in/handle/avu/2258 | |
| dc.lang | English | en_US |
| dc.publisher.name | International Journal of Computer Applications | en_US |
| dc.publisher.type | International | en_US |
| dc.title | Enhanced Fuzzy Roughset based Feature Selection Strategy using Differential Evolution | en_US |
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