Skip to main content

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 [28–30]

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.