TY  - JOUR
T1  - Multiclass Cancer Diagnosis using Firefly Algorithm and K- Nearest Neighbor
AU  - Pashaeı, Elnaz
PY  - 2018
DA  - April
JF  - International Journal of Electronics Mechanical and Mechatronics Engineering
JO  - IJEMME
PB  - Istanbul Aydin University
WT  - DergiPark
SN  - 2146-0604
SP  - 1537
EP  - 1542
VL  - 8
IS  - 2
LA  - en
AB  - Abstract - Among a large number of genes in microarray data sets that characterize the samples, many of them may be irrelevantto the learning tasks. Thus there is a need for reliable methods for gene representation, reduction, and selection, to speed up theprocessing rate, improve the classification accuracy, and to avoid incomprehensibility due to the high number of genes investigated.Classifying multiclass data sets is usually more difficult than classifying microarray datasets with only two classes. In this paper,we propose a new gene selection and classification strategy based on Firefly Algorithm (FFA) and K- Nearest Neighbor (KNN),suitable for multiclass microarray data sets. This approach is associated with Kruskal-test pre-filtering technique. The FFA isutilized to evolve gene subsets whose fitness is evaluated by a KNN classifier with leave-one-out-cross-validation (LOOCV)schema. The experimental results on three multiclass high-dimensional data sets show that the proposed method simplifies genesignatures effectively and obtains approximately higher classification accuracy compared to the best previously published results.
KW  - Gene selection
KW  - firefly algorithm
KW  - kruskal-test
KW  - k- nearest neighbor
CR  - References
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UR  - https://dergipark.org.tr/en/pub/ijemme/issue//545950
L1  - https://dergipark.org.tr/en/download/article-file/682051
ER  -