CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures

TL;DR

CACTUS is an explainable AI tool designed to improve secure analytics by effectively classifying data structures, supporting categorical attributes, and providing insights into attribute importance, with demonstrated effectiveness on medical datasets.

Contribution

The paper introduces CACTUS, a novel tool that enhances explainability and efficiency in data classification, especially for categorical attributes, with applications to medical datasets.

Findings

Supports categorical attributes while preserving their meaning

Optimizes memory usage and computation speed through parallelization

Effectively ranks attributes by discriminative power

Abstract

The availability of large data sets is providing an impetus for driving current artificial intelligent developments. There are, however, challenges for developing solutions with small data sets due to practical and cost-effective deployment and the opacity of deep learning models. The Comprehensive Abstraction and Classification Tool for Uncovering Structures called CACTUS is presented for improved secure analytics by effectively employing explainable artificial intelligence. It provides additional support for categorical attributes, preserving their original meaning, optimising memory usage, and speeding up the computation through parallelisation. It shows to the user the frequency of the attributes in each class and ranks them by their discriminative power. Its performance is assessed by application to the Wisconsin diagnostic breast cancer and Thyroid0387 data sets.