A new fitness function in genetic programming for classification of imbalanced data
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- @Article{Kumar:JETAI,
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author = "Arvind Kumar",
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title = "A new fitness function in genetic programming for
classification of imbalanced data",
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journal = "Journal of Experimental \& Theoretical Artificial
Intelligence",
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keywords = "genetic algorithms, genetic programming, Unbalanced
data classification, fitness function, imbalanced
classification, class imbalance",
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publisher = "Taylor \& Francis",
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DOI = "doi:10.1080/0952813X.2022.2120087",
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size = "13 pages",
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abstract = "Many real-world problems have an uneven distribution
of data over different classes. The imbalanced nature
of data impacts the performance of classifiers. The
higher counts of majority class samples influence the
learning abilities of well-known classifiers. Genetic
programming (GP) algorithm based on natural evolution
also impacts if the data nature is imbalanced. The
fitness function plays a pivotal role and impacts
almost each building block of the GP framework. GP with
the standard fitness function produces under-fitted and
biased classifiers. Therefore, this paper has proposed
a new fitness function in GP to classify the imbalanced
data. The proposed method is used to classify nine
imbalanced problems: ABL-18, ABL-9-18, BAL, YEAST2,
YEAST1, ABL-9, ION, WDBC, and SPECT. The imbalanced
factor of benchmark problems varies from 99:1 to 59:41.
The proposed method performance is compared with
K-Nearest-Neighbourhood (KNN) and the standard fitness
function-based GP methods. The GP with newly proposed
fitness function gives average AUC values for
ABL-18(99:1), ABL-9-18(94:6), BAL(92:8), YEAST2(89:11),
YEAST1(84:16), ABL-9(83:17), ION(64:36), WDBC(63:37),
and SPECT(59:41) as 0.714, 0.812, 0.975, 0.916, 0.768,
0.654, 0.872, 0.939, and 0.704, respectively, which are
higher than KNN and the standard fitness function-based
GP methods. The result outcomes prove the superiority
of the proposed method.",
- }
Genetic Programming entries for
Arvind Kumar
Citations