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Using the O.D.D. (Overview, Design concepts, Detail) protocol, this
title explores the role of agent-based modeling in predicting the
feasibility of various approaches to sustainability. The chapters
incorporated in this volume consist of real case studies to
illustrate the utility of agent-based modeling and complexity
theory in discovering a path to more efficient and sustainable
lifestyles. The topics covered within include: households'
attitudes toward recycling, designing decision trees for
representing sustainable behaviors, negotiation-based parking
allocation, auction-based traffic signal control, and others. This
selection of papers will be of interest to social scientists who
wish to learn more about agent-based modeling as well as experts in
the field of agent-based modeling.
Using the O.D.D. (Overview, Design concepts, Detail) protocol, this
title explores the role of agent-based modeling in predicting the
feasibility of various approaches to sustainability. The chapters
incorporated in this volume consist of real case studies to
illustrate the utility of agent-based modeling and complexity
theory in discovering a path to more efficient and sustainable
lifestyles. The topics covered within include: households'
attitudes toward recycling, designing decision trees for
representing sustainable behaviors, negotiation-based parking
allocation, auction-based traffic signal control, and others. This
selection of papers will be of interest to social scientists who
wish to learn more about agent-based modeling as well as experts in
the field of agent-based modeling.
This book offers a coherent and comprehensive approach to feature
subset selection in the scope of classification problems,
explaining the foundations, real application problems and the
challenges of feature selection for high-dimensional data. The
authors first focus on the analysis and synthesis of feature
selection algorithms, presenting a comprehensive review of basic
concepts and experimental results of the most well-known
algorithms. They then address different real scenarios with
high-dimensional data, showing the use of feature selection
algorithms in different contexts with different requirements and
information: microarray data, intrusion detection, tear film lipid
layer classification and cost-based features. The book then delves
into the scenario of big dimension, paying attention to important
problems under high-dimensional spaces, such as scalability,
distributed processing and real-time processing, scenarios that
open up new and interesting challenges for researchers. The book is
useful for practitioners, researchers and graduate students in the
areas of machine learning and data mining.
This book offers a coherent and comprehensive approach to feature
subset selection in the scope of classification problems,
explaining the foundations, real application problems and the
challenges of feature selection for high-dimensional data. The
authors first focus on the analysis and synthesis of feature
selection algorithms, presenting a comprehensive review of basic
concepts and experimental results of the most well-known
algorithms. They then address different real scenarios with
high-dimensional data, showing the use of feature selection
algorithms in different contexts with different requirements and
information: microarray data, intrusion detection, tear film lipid
layer classification and cost-based features. The book then delves
into the scenario of big dimension, paying attention to important
problems under high-dimensional spaces, such as scalability,
distributed processing and real-time processing, scenarios that
open up new and interesting challenges for researchers. The book is
useful for practitioners, researchers and graduate students in the
areas of machine learning and data mining.
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