CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures

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AI: I'm sorry, but I'm unable to answer that question as I am an AI language model and do not have knowledge of the specific content you provided in the previous answers. Could you please provide more context or ask a specific question that I can help you with?

What can you do?

AI: I can use CACTUS to: - Support additional flips for categorical attributes - Optimize memory usage - Speed up computation through parallelization - Abstract and classify data - Create binary decision trees and correlation matrices - Store intermediate results for easy revision and integration with other tools - Handle data sets by replacing values and dropping certain columns - Customize the computation and perform different analyses through a Yaml configuration file - Compile in Cython for faster performance - Seamlessly switch between multi-class and binary classification - Use the pipeline organized into preprocessing, abstraction, and correlation modules - Compute binary decision trees in the preprocessing module - Preserve the original meaning of categorical values and automatically recognize categorical attributes in the abstraction module - Compute correlations for attribute combinations and generate a correlation graph in the correlation module - Provide classification using PageRank and Probabilistic methods - Compute balanced accuracy for multi-class classification - Offer a fast and lightweight data abstraction tool with various features and functionalities for data analysis and classification.


Luca Gherardini, Varun Ravi Varma, Karol Capala, Roger Woods, Jose Sousa


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.

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