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A Transdisciplinary Approach to Enhancing Online Engineering Education Through Learning Analytics Masikini Lugoma , Lethuxolo Yende , Pule Dikgwatlhe , Akhona Mkonde , Rorisang Thage , Lucky Maseko , Ngonidzashe Chimwani (pages: 1-6) AI Disruptions in Higher Education: Evolutionary Change, Not Revolutionary Overthrow Cristo Leon , James Lipuma , Maximus Rafla (pages: 7-18) Education, Research, and Methodology: A Transdisciplinary Cybernetic Whole Nagib Callaos , Cristo Leon (pages: 19-33) Enhancing Educational Effectiveness Through Transdisciplinary Practice: The ETCOP Model Birgit Oberer , Alptekin Erkollar , Andreas Kropfberger (pages: 34-40) From Instruction to Interaction: Reflexive Learning Design for Cross-Generational Engagement at the Workplace Gita Aulia Nurani , Ya-Hui Lee (pages: 41-44) GIS in Aquatic Animal Health Surveillance: A Transdisciplinary eLearning Initiative Integrating Education, Research, and Methodology (The Aquae Strength Project) Eleonora Franzago , Rodrigo Macario , Matteo Mazzucato , Federica Sbettega , Manuela Cassani , Guido Ricaldi , Francesco Bissoli , Anna Nadin , Fabrizio Personeni , Manuela Dalla Pozza , Grazia Manca , Nicola Ferré (pages: 45-50) Reflexivity as a Compass: The European AI Act and Its Implications for U.S. Higher Education Institutions Jasmin Cowin (pages: 51-56) Required General Education Program Evaluation: Bridging the Gap Between Educators and Administrators James Lipuma , Cristo Leon , Jeremy Reich (pages: 57-61) Researching Ourselves Jeremy Horne (pages: 62-72) The Self-Aware, Reflective Learner: Fostering Metacognitive Awareness and Reflexivity in Undergraduates Through Service-Learning Genejane Adarlo (pages: 73-81)
ABSTRACT
Efficient Pruning Method for Ensemble Self-Generating Neural Networks
Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.
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