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Table 10 Comparison of best classification accuracy for the Leukemia dataset 1

From: Accurate molecular classification of cancer using simple rules

Methods (feature selection + classification)a #Selected genes #Correctly classified samples (accuracy) Rule-based classifier
depended degree + decision rules [this work] 1 31 (91.18%) yes
  2 34 (100%)  
t-test, attribute reduction + decision rules [7] 1 31 (91.18%) yes
attribute reduction + k-NNs [9] 2 33 (97.06%) no
rough sets, GAs + k-NNs [10] 9 31 (91.18%) no
EPs [6] 1 31 (91.18%) yes
discretization + decision trees [11]b unknownc 31 (91.18%) yes
CBF + decision trees [24] 1 31 (91.18%) yes
TSP [14] 2 31 (91.18%) yes
RCBT [13] 10-40 31 (91.18%) yes
neighborhood analysis + weighted voting [2] 50 29 (85.29%) no
signal to noise ratios + PNNs [23] 50 34 (100%) no
MAMA [25] 132-549 34 (100%) no
PLS + LD or QDA [26] 50-1500 28-33 (82.4%-97%) no
prediction strength + SVMs [27] 25-1000 30-32 (88.2%-94.1%) no
SVMs [2830] 8-30 34 (100%) no
  1. aThe text before "+" states the feature selection method, while that after it states the classification method. The absence of "+" means that the same method was used for both feature selection and classification.
  2. bThe decision trees are also involved in feature selection.
  3. c"unknown" means that no related data are provided in the article.
  4. These explanations apply to the other tables.