ArticleEvolutionary intelligence2015
ExSTraCS 2.0: Description and Evaluation of a Scalable Learning Classifier System.
Article in Evolutionary intelligence, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- Survival-LCS: Rule-Based Survival Analysis without Proportional Hazard Assumptions.ACM transactions on evolutionary learning and optimization · 2026Article
- Audience-Customized Translation of Rule-Based Evidence with Large Language Models Across Multiplexer Benchmarks.Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference · 2026Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Deep Learning-Based Fault Diagnosis via Multisensor-Aware Data for Incipient Inter-Turn Short Circuits (ITSC) in Wind Turbine Generators.Sensors (Basel, Switzerland) · 2025Article
- Rule-based Machine Learning: Separating Rule and Rule-Set Pareto-Optimization for Interpretable Noise-Agnostic Modeling.Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference · 2025Article
- A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA.Research square · 2024Article
- IMPatienT: An Integrated Web Application to Digitize, Process and Explore Multimodal PATIENt daTa.Journal of neuromuscular diseases · 2024Article
- Mental stress recognition on the fly using neuroplasticity spiking neural networks.Scientific reports · 2023Article
- Feature weighted models to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.Bioinformatics (Oxford, England) · 2023Article
- Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors.JCO clinical cancer informatics · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Breathing as an Input Modality in a Gameful Breathing Training App (Breeze 2): Development and Evaluation Study.JMIR serious games · 2022Article
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- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
- Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset.JAMA network open · 2021Article
- In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity.International journal of molecular sciences · 2021Article
- Melanoma diagnosis using deep learning techniques on dermatoscopic images.BMC medical imaging · 2021Article
- Computer-Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI.Journal of magnetic resonance imaging : JMRI · 2020Article
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Authors and funding
2 authors.
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Abstract
Algorithmic scalability is a major concern for any machine learning strategy in this age of 'big data'. A large number of potentially predictive attributes is emblematic of problems in bioinformatics, genetic epidemiology, and many other fields. Previously, ExS-TraCS was introduced as an extended Michigan-style supervised learning classifier system that combined a set of powerful heuristics to successfully tackle the challenges of classification, prediction, and knowledge discovery in complex, noisy, and heterogeneous problem domains. While Michigan-style learning classifier systems are powerful and flexible learners, they are not considered to be particularly scalable. For the first time, this paper presents a complete description of the ExS-TraCS algorithm and introduces an effective strategy to dramatically improve learning classifier system scalability. ExSTraCS 2.0 addresses scalability with (1) a rule specificity limit, (2) new approaches to expert knowledge guided covering and mutation mechanisms, and (3) the implementation and utilization of the TuRF algorithm for improving the quality of expert knowledge discovery in larger datasets. Performance over a complex spectrum of simulated genetic datasets demonstrated that these new mechanisms dramatically improve nearly every performance metric on datasets with 20 attributes and made it possible for ExSTraCS to reliably scale up to perform on related 200 and 2000-attribute datasets. ExSTraCS 2.0 was also able to reliably solve the 6, 11, 20, 37, 70, and 135 multiplexer problems, and did so in similar or fewer learning iterations than previously reported, with smaller finite training sets, and without using building blocks discovered from simpler multiplexer problems. Furthermore, ExS-TraCS usability was made simpler through the elimination of previously critical run parameters.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.