Feature ranking through weights manipulations for artificial neural networks-based classifiers

Artificial Neural Networks (ANNs) are often viewed as black box. This limits the comprehensive understanding on how it deals with input neuron/data, as well as how it reached a particular decision. Input significance analysis (ISA) refers to the process of understanding these input neurons/data. And since this work is on classification problem, hence similarly, this process can also be called feature selection; where the goal is to have a classifier that can predict accurately and at the same time, its structure is as simple as possible. This work is particularly interested with ISA methods that manipulate weights, where separately, correlations are also applied. The goal is to create feature ranking list that performed the best in the selected classifiers. For validation methods, memory recall validation and K-Fold cross-validation methods are used. The results show one classifier that uses one of the ISA methods are performing well for both validation methods