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Table 5 TOPSIS scores of the prediction performance for the three different kernels

From: PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel

Kernel Q1 Q2 Q3 Q4 Overall
Linear 54.64 67.50 45.98 47.56 70.09
Laplace 61.12 59.85 58.11 41.67 73.20
Radial 40.78 31.75 24.98 57.91 23.77
  1. For Q1 and Q3, TOPSIS scores are higher for the Laplace kernel, whereas linear and RBF achieved higher scores for Q2 and Q4 respectively. While all the four sub-datasets are accounted, the Laplace kernel achieved higher TOPSIS score than the other two kernel functions