Extended Supervised Tracking and Classification System (scikit-ExSTraCS)

Ray — Sun, 05 Mar 2023 20:44:22 +0000

The scikit-ExSTraCS package includes a sklearn-compatible Python implementation of ExSTraCS 2.0. ExSTraCS 2.0, or Extended Supervised Tracking and Classifying System, implements the core components of a Michigan-Style Learning Classifier System (where the system’s genetic algorithm operates on a rule level, evolving a population of rules with each their own parameters) in an easy to understand way, while still being highly functional in solving ML problems. It allows the incorporation of expert knowledge in the form of attribute weights, attribute tracking, rule compaction, and a rule specificity limit, that makes it particularly adept at solving highly complex problems. In general, Learning Classifier Systems (LCSs) are a classification of Rule Based Machine Learning Algorithms that have been shown to perform well on problems involving high amounts of heterogeneity and epistasis. Well designed LCSs are also highly human interpretable. LCS variants have been shown to adeptly handle supervised and reinforced, classification and regression, online and offline learning problems, as well as missing or unbalanced data. These characteristics of versatility and interpretability give LCSs a wide range of potential applications, notably those in biomedicine.

HEROS (Heuristic Evolutionary Rule Optimization System)

Elizabeth — Thu, 11 Dec 2025 20:55:46 +0000

HEROS (Heuristic Evolutionary Rule Optimization System) is an evolutionary rule-based machine learning (ERBML) algorithm framework for supervised learning. This scikit-learn compatible machine learning modeling package is designed to agnostically model simple/complex and/or clean/noisy problems (without hyperparameter optimization) and yield maximally human interpretable models. HEROS adopts a two-phase approach separating rule optimization, and rule-set (i.e. model) optimization, each with distinct multi-objective Pareto-front-based optimization. Rules are optimized based on maximizing rule-accuracy and instance coverage using a Pareto-inspired rule fitness function. Differently, models are optimized based on maximizing balanced accuracy and minimizing rule-set size using an NSGA-II-inspired evolutionary algorithm.

Machine Learning Modeling – Penn AI Tech

Extended Supervised Tracking and Classification System (scikit-ExSTraCS)

HEROS (Heuristic Evolutionary Rule Optimization System)