mlsanityData to 14 Oct 2026

CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures

arXiv 2308.12031 ↗First posted 23 Aug 2023cs.LG cs.AIcited by 3

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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.

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

9 of 49 references.

References · 9

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  9. 2020Self-Weighted Robust LDA for Multiclass Classification with Edge Classescited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degenerationcited by 1
  2. 2026A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical datacited by 0
  3. 2026CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphscited by 0