首页|New Machine Learning Findings from University of Science & Technology Liaoning Reported (A Novel Hybrid Binary Whale Optimization Algorithm With Chameleon Hunting Mechanism for Wrapper Feature Selection In Qsar Classification Model: a ...)
New Machine Learning Findings from University of Science & Technology Liaoning Reported (A Novel Hybrid Binary Whale Optimization Algorithm With Chameleon Hunting Mechanism for Wrapper Feature Selection In Qsar Classification Model: a ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Current study results on Machine Learning have been published. According to news reporting fromAnshan, People’s Republic of China, by NewsRx journalists, research stated, “High dimensionality is one ofthe main challenges in Quantitative Structure-Activity Relationship (QSAR) classification modeling, andfeature selection as an effective dimensionality reduction method plays an important role in machine learning,particularly in fields such as chemometrics. In this paper, for feature selection in QSAR classificationmodeling, a hybrid whale optimization algorithm (WOA) with a chameleon hunting mechanism (HWOACHM)is proposed, and its binary version is used to find the best subset for wrapper feature selection inthe QSAR classification model.”
AnshanPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningUniversity of Science & Technology Liaoning
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