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Izvorni znanstveni članak
https://doi.org/10.20532/cit.2017.1003294

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Arpita Nagpal ; Department of Computer Science and Engineering, School of Engineering and Technology, The NorthCap University, Gurugram, Haryana, India
Deepti Gaur ; Department of Computer Science and Engineering, School of Engineering and Technology, The NorthCap University, Gurugram, Haryana, India

Puni tekst: engleski, pdf (337 KB) str. 133-148 preuzimanja: 385* citiraj
APA 6th Edition
Nagpal, A. i Gaur, D. (2017). Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data. Journal of computing and information technology, 25 (2), 133-148. https://doi.org/10.20532/cit.2017.1003294
MLA 8th Edition
Nagpal, Arpita i Deepti Gaur. "Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data." Journal of computing and information technology, vol. 25, br. 2, 2017, str. 133-148. https://doi.org/10.20532/cit.2017.1003294. Citirano 29.10.2020.
Chicago 17th Edition
Nagpal, Arpita i Deepti Gaur. "Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data." Journal of computing and information technology 25, br. 2 (2017): 133-148. https://doi.org/10.20532/cit.2017.1003294
Harvard
Nagpal, A., i Gaur, D. (2017). 'Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data', Journal of computing and information technology, 25(2), str. 133-148. https://doi.org/10.20532/cit.2017.1003294
Vancouver
Nagpal A, Gaur D. Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data. Journal of computing and information technology [Internet]. 2017 [pristupljeno 29.10.2020.];25(2):133-148. https://doi.org/10.20532/cit.2017.1003294
IEEE
A. Nagpal i D. Gaur, "Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data", Journal of computing and information technology, vol.25, br. 2, str. 133-148, 2017. [Online]. https://doi.org/10.20532/cit.2017.1003294

Sažetak
Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP) for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure) uses a firefly optimization algorithm in the local search optimzation phase of the greedy randomized adaptive search procedure (GRASP). Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios) which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FFGRASP performs better on seven out of nine datasets.

Ključne riječi
feature selection; microarray; classification; GRASP; hill climbing; firefly algorithm; tabu search

Hrčak ID: 183329

URI
https://hrcak.srce.hr/183329

Posjeta: 503 *