CACTUS: a comprehensive abstraction and classification tool for uncovering structures

The availability of large data sets is providing the impetus for driving many current artificial intelligent developments. There are, however, specific challenges in developing solutions exploiting small data sets due to practical and costeffective deployment and the opacity of deep learning models. To address this, the Comprehensive Abstraction and Classification Tool for Uncovering Structures called CACTUS is presented as a means of improving secure analytics by effectively employing explainable artificial intelligence. It does this by providing additional support for categorical attributes, preserving their original meaning, optimising memory usage, and speeding up the computation through parallelisation. It exposes to the user the frequency of the attributes in each class and ranks them by their discriminative power. Its performance is assessed by applying it to various domains including the Wisconsin diagnostic breast cancer, Thyroid0387, mushroom, heart disease, and adult income data sets.