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Titlebook: Artificial Intelligence in Theory and Practice II; IFIP 20th World Comp Max Bramer Conference proceedings 2008 IFIP International Federatio

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发表于 2025-3-21 17:33:51 | 显示全部楼层 |阅读模式
期刊全称Artificial Intelligence in Theory and Practice II
期刊简称IFIP 20th World Comp
影响因子2023Max Bramer
视频video
发行地址Peer-reviewed and carefully selected.Much information in this series is published in advance of journal publication.The contributors in this volume are world-renowned experts in their field.Includes s
学科分类IFIP Advances in Information and Communication Technology
图书封面Titlebook: Artificial Intelligence in Theory and Practice II; IFIP 20th World Comp Max Bramer Conference proceedings 2008 IFIP International Federatio
影响因子The papers in this volume comprise the refereed proceedings of the conference ‘ Artificial Intelligence in Theory and Practice’ (IFIP AI 2008), which formed part of the 20th World Computer Congress of IFIP, the International Federation for Information Processing (WCC-2008), in Milan, Italy in September 2008. The conference is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (Artificial Intelligence Applications). All papers were reviewed by at least two members of our Program Committee. Final decisions were made by the Executive Program Committee, which comprised John Debenham (University of Technology, Sydney, Australia), Ilias Maglogiannis (University of Aegean, Samos, Greece), Eunika Mercier-Laurent (KIM, France) and myself. The best papers were selected for the conference, either as long papers (maximum 10 pages) or as short papers (maximum 5 pages) and are included in this volume. The international nature of IFIP is amply reflected in the large number of countries represented here. The conference also featured invited talks by Prof. Nikola Kasabov (Auckland University of Technology, New Zealand) and Prof.
Pindex Conference proceedings 2008
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Enhancing RBF-DDA Algorithm’s Robustness: Neural Networks Applied to Prediction of Fault-Prone Softwnd (ii) to compare RBF-eDDA and MLP neural networks in software defects prediction. The simulations reported in this paper show that RBF-eDDA is able to correctly handle inconsistent patterns and that it obtains results comparable to those of MLP in the NASA data sets.
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Conference proceedings 2008formed part of the 20th World Computer Congress of IFIP, the International Federation for Information Processing (WCC-2008), in Milan, Italy in September 2008. The conference is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (
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A Study with Class Imbalance and Random Sampling for a Decision Tree Learning Systemggest that altering the class distribution can improve the classification performance of classifiers considering AUC as a performance metric. Furthermore, as a general recommendation, random over-sampling to balance distribution is a good starting point in order to deal with class imbalance.
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