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The average number of remove attributes of all discretization algorithms is summarized in Fig. 11c.

Some discretization algorithms are an implicit coupling with supervised feature selection, especially EMD, ChiMerge, and MChi2 as shown in Fig. 15c, average number of remove attributes.

The results of predictive accuracy, number of intervals, number of remove attributes, and running times of 13 discretization algorithms of 30 standard datasets (tenfold) and 4 classifiers.

The results of AUC, number of intervals, number of remove attributes, and running times of 13 discretization algorithms of 20 imbalanced datasets (fivefold) and 4 classifiers.

The details of the result are included as an Additional file 3. Fig. 15 Discretization results of: a average number of intervals, b average running times (seconds), and c average number of remove attributes for imbalanced datasets.

The details of the result are included as an Additional file 2. Fig. 11 Discretization results of: a average number of intervals, b average running times (seconds), and c average number of remove attributes for standard datasets.

The morphological quantitative attributes of the master horizons A, Bw, Bg, Bk and C were first reduced in number so as to remove attribute redundancy.

Given an attribute subset (Bsubseteq A) and a decision attribute (d), the projection of the object set (U) on (B) is denoted by (Pi _{B }(U)) and can be computed by the following two steps: removing attributes in the different set ((A-B)) and merging all the remaining rows which are indiscernible [11].

We removed attributes for which we had fewer than 20 linked pairs with positive values according to the MIPS network.

Moore and White (2007b) proposed a 'tuned' ReliefF algorithm (TuRF) that systematically removes attributes that have low-quality estimates so that the ReliefF values if the remaining attributes can be reestimated.

The second mechanism, adaptive removing attribute (ARA) mechanism, removes some redundant attributes in the process of selecting node.