Table 8 Kappa statistics for training data sets for various Autoweka/Weka feature selection settings.

From: Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling

Therapy

No filter

cfs-best

cfs-greedy

Corr-ranker

Gain-ranker

j48-ranker

j48-greedy

CVN (lh)

Yesnoyes

0.30 (rf)

0.52 (mp)

0.52 (mp)

0.23 (lo)

0.38 (smo)

0.33 (lwl)

0.58 (mp)

0.52 (2)

Nonoyes

0.35 (sl)

0.34 (nb)

0.15 (rf)

0.22 (smo)

0.16 (lwl)

0.35 (bn)

0.15 (rf)

0.58 (2)

Nonono

0.09 (rf)

0.64 (bn)

0.5 (smo)

0.35 (ibk)

0.35 (nb)

0.35 (nb)

0.50 (rf)

0.44 (4)

Yesnono

0.7 (rf)

0.53 (sgd)

0.69 (rf)

0.39 (lo)

0.56 (rf)

0.56 (rf)

0.7 (rf)

0.53 (1)

Yesyesno

− 0.09 (dt)

0.36 (rc)

0.05 (nb)

0.22 (lwl)

0.10 (lwl)

0.0 (ab)

0.05 (nb)

0.48 (m)

Yesyesyes

− 0.07 (nbm)

− 0.07 (ibk)

− 0.01 (ibk)

0.19 (smo)

0.14 (lwl)

0.26 (rss)

− 0.07 (ibk)

0.51 (3)

Noyesno

− 0.26 (rf)

− 0.03 (mp)

− 0.03 (mp)

0.11 (mp)

− 0.26 (smo)

− 0.22 (mp)

− 0.03 (mp)

0.41 (2)

Noyesyes (*)

0.0 (rpt)

− 0.53 (rf)

− 0.53 (rf)

0.13 (rf)

0.17 (rf)

0.23 (rf)

− 0.54 (rf)

0.60 (m)

  1. Therapy class (RAD, CHE, HOR). lh lookahead number or manually determined (m). Legend for autoweka methods: rf random forest, mp multilevel perceptron, nb Naive Bayes, bn Bayes Net, sgd stochastic gradient descent, rc random committee, ibk k-nearest neighbour classifier, sl simple logistic, nbm Naive Bayes Multinomial, rpt Fast Decision Tree REPTree (C4.5), smo fast training support vector machine, lo Logistic, lwl Locally Weighted Learning, ab AdaBoostM1, rss random subspace, dt decision table. (*) result for the validation dataset.