# CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures

arXiv: https://arxiv.org/abs/2308.12031 · HTML: https://mlsanity.com/paper/2308.12031/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 23 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Towards Explainable Deep Neural Networks (xDNN)](https://mlsanity.com/paper/1912.02523/) (2019)
- [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022)
- [Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models](https://mlsanity.com/paper/2304.03271/) (2023)
- [Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models](https://mlsanity.com/paper/1310.4378/) (2013)
- [Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis](https://mlsanity.com/paper/2203.02202/) (2022)
- [The fundamental nature of the log loss function](https://mlsanity.com/paper/1502.06254/) (2015)
- [Self-Weighted Robust LDA for Multiclass Classification with Edge Classes](https://mlsanity.com/paper/2009.12362/) (2020)

## Built on by (most cited first, 3 of 3)

- [CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration](https://mlsanity.com/paper/2506.14843/) (2025)
- [A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data](https://mlsanity.com/paper/2602.17364/) (2026)
- [CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2607.19996/) (2026)

## Abstract (arXiv)

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.