Abstract
The availability of large datasets is providing the impetus for driving many current artificial intelligent developments. However, specific challenges arise in developing solutions that exploit small datasets, mainly due to practical and cost-effective deployment issues, as well as the opacity of deep learning models. To address this, the Comprehensive Abstraction and Classification Tool for Uncovering Structures (CACTUS) is presented as a means of improving secure analytics by effectively employing explainable artificial intelligence. CACTUS achieves 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. Performance is assessed by applying it to various domains, including Wisconsin Diagnostic Breast Cancer, Thyroid0387, Mushroom, Cleveland Heart Disease, and Adult Income datasets.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
