Ensemble Case Based Learning for
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This book presents a framework for machine learning in a distributed data scenario with decentralized decision making. We have based our framework in Multi-Agent Systems (MAS), and in Case-Based Reasoning (CBR). Moreover, we are interested in autonomous agents that collaboratively work as ensembles. Specifically, we present the Multi-Agent Case Based Reasoning (MAC) framework, a multi-agent approach to CBR. Each individual agent in a MAC system is capable of individually learn and solve problems using CBR with an individual case base. Moreover, each case base is owned and managed by an individ...
This book presents a framework for machine learning in a distributed data scenario with decentralized decision making. We have based our framework in Multi-Agent Systems (MAS), and in Case-Based Reasoning (CBR). Moreover, we are interested in autonomous agents that collaboratively work as ensembles. Specifically, we present the Multi-Agent Case Based Reasoning (MAC) framework, a multi-agent approach to CBR. Each individual agent in a MAC system is capable of individually learn and solve problems using CBR with an individual case base. Moreover, each case base is owned and managed by an individual agent, and any information is disclosed or shared only if the agent decides so. Thus, this framework preserves the privacy of data, and the autonomy to disclose data.The focus of this book is to develop strategies so that individual learning agents improve their performance both individually and as an ensemble. The techniques presented fall in between the fields of CBR and MAS, and thus they aim at providing insights on how to successfully combine both fields.
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