.png){kind=icon}
.png){kind=icon}
Return
Feature Selection for Optimized High-Dimensional Biomedical Data Using an Improved Shuffled Frog Leaping Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/TCBB.2016.2602263.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy, and an adaptive transfer factor. Our proposed approach explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.
Keywords:
Shuffled frog leaping algorithm
feature selection
k-nearest neighbor
classification accuracy
biomedical data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.4
Papers:
3.3K
Citations:
6.4K