Table 5: Optimal FS methods for each dataset.

FS accuracy %FMESSILR
Feature selection methodCFSChiSMRMRWSACFSChiSMRMRWSACFSChiSMRMRWSACFSChiSMRMRWSA

Original no. of attributes74747474686868688888888875757575
No. of attributes after FS8553020175330212071302932323031
Classification algorithm
 J4863.178364.728763.565967.441961.2559.37557.564.37573.161866.176574.632464.70599492.33337094
 BFTree71.705468.992262.015576.356658.12556.256565.62583.088279.779485.661881.2593.333391.66666893.3333
 FT74.03179.069872.868282.558164.37578.12565.62588.7597.426588.970693.7588.235372.66677169.333376
 LMT63.565992.441973.643497.674460.62583.1257088.12596.323592.446591.911887.867686.66678566.666786.6667
 NBTree71.705463.178363.178370.15551.2559.37567.566.25n/an/an/an/a67.333365.666666.666768
 RandomForest73.643470.542667.829572.868261.87574.37571.2573.12590.441274.264781.985381.985390.66678970.666790.6667
 RandomTree64.728759.689955.81470.1554566.2557.567.569.117661.764769.485363.970685.333383.666684.666785.3333
 REPTree72.09372.09367.441973.643456.87561.256064.37584.926584.191279.411883.455994.66679366.666794.6667
 ConjunctiveRule55.81455.81448.837265.891554.375555556.25n/an/an/an/a66.66676565.333366.6667
 DecisionTable70.15570.15553.87668.604757.556.2561.87552.567.279458.823563.235359.558893.333391.666677.333393.3333
 FURIA71.705477.131863.953574.806262.568.7564.37553.12580.514762.578.676586.39718482.33337084
 JRip72.868271.705467.829573.643466.2554.37558.1255574.264734.926572.058865.44129896.333366.666798
 NNge68.992266.666755.81463.565953.12546.2552.550.62592.647191.176592.279481.2587.333385.666669.333387.3333
 OneR50.387650.387654.263654.263654.37554.37554.37561.2555.147155.147149.264749.264793.333391.66665493.3333
 PART64.341168.604762.403168.604758.7556.87558.12570.62573.529467.279480.514771.32359492.333369.333394
 NaiveBayes70.930264.728767.441967.054364.37568.7558.7558.7595.220676.838286.029472.058878.6667776478.6667
 Bagging73.255875.193870.15575.96963.7564.37568.7553.12589.338285.661884.926586.397193.333391.666666.666794.6667
 LibSVM66.666787.596963.565989.9225n/an/an/an/an/an/an/an/a57.333355.666646.666757.3333
 MultilayerPerceptron68.604779.069872.480689.534963.7570.62561.87566.87591.911893.7593.7586.397110098.333370100
 SMO72.480677.90773.643479.457476.87575.62561.87567.593.014793.7593.382480.51479896.33337699.3333
Mean accuracy %68.0426370.7848964.0310274.10853559.7368463.6513261.5789564.4078982.7854774.5556880.6444875.88668 86.4333384.7666367.9000186.76667
Time (s)0.782.8673.5631.275 1.033.3281.439441.4761.913.8153.2635851.392.174.8906