To determine the most appropriate attributes that distinguish between normal traders and fraudsters, a modified wrapper procedure is developed to select a subset of attributes from a large candidate attribute pool.
Testor theory is a convenient way to solve this problem since a testor is defined as a subset of attributes that can discern between objects from different classes; and an irreducible testor is a minimal subset with this property.
A process-mediated data source provides a subset of objects in a particular domain and values of a subset of attributes for each object.
The indiscernibility relation for a subset of attributes (B) is defined as: begin{aligned} mathrm{IND}(B ={(x,y in Utimes Uleft| {;forall ain B,;f_a (x)=f_a (y)} right.
Next, we identified a subset of attributes to be targeted first.
A subset of attributes is then selected from this low dimensional space.
FS techniques identify a subset of attributes, for example genes or proteins, which could be used to build a more successful model, compared to using the whole dataset.
The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression.
The sets of observed and simulated states S obs and S sim were characterised by the expression levels of each gene, i.e., by a subset of attributes M = E × F, with entities or genes E, and the corresponding values F = {off, on}.
If Q is the decision attribute D, and P is a subset of condition attributes, then γ P (D) represents the depended degree of the condition attribute subset P by the decision attribute D; that is, to what degree P can discriminate the distinct classes of D. In this sense, γ P (D) reflects the classification power of the subset P of attributes.
