Multi-Agent Architecture for Knowledge Discovery

Knowledge discovery from databases (KDD) is a complex process composed of several phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. For each of the phases, there are many algorithms and methods available, the end-user having to select one of them. The AgentDiscover is a multi-agent based intelligent recommendation system for selection of the most appropriate solving method for each phase. This brings added value for both novice and experienced users

[1]  Todd R. Johnson,et al.  Generic tasks and task structures: history, critique and new directions , 1993 .

[2]  Thomas Reinartz,et al.  CRISP-DM 1.0: Step-by-step data mining guide , 2000 .

[3]  Wei Zhong Liu,et al.  Bias in information-based measures in decision tree induction , 1994, Machine Learning.

[4]  Giovanna Castellano,et al.  Meta-data: Characterization of Input Features for Meta-learning , 2005, MDAI.

[5]  M. Nikraz,et al.  A methodology for the analysis and design of multi-agent systems using JADE , 2006 .

[6]  Reid Simmons,et al.  Second Generation Expert Systems , 1993, Springer Berlin Heidelberg.

[7]  Abraham Bernstein,et al.  Toward intelligent assistance for a data mining process: an ontology-based approach for cost-sensitive classification , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  Viorel Negru,et al.  An Extensible Environment for Expert System Development , 2003, KES.

[9]  Foster J. Provost,et al.  A Survey of Methods for Scaling Up Inductive Algorithms , 1999, Data Mining and Knowledge Discovery.

[10]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[11]  Qing He,et al.  Execution Engine of Meta-learning System for KDD in Multi-agent Environment , 2005, AIS-ADM.

[12]  David W. Aha,et al.  A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.

[13]  Antonio F. Gómez-Skarmeta,et al.  METALA: A Meta-learning Architecture , 2001, Fuzzy Days.

[14]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.

[15]  Hsinchun Chen,et al.  Design and evaluation of a multi-agent collaborative Web mining system , 2003, Decis. Support Syst..

[16]  Pericles A. Mitkas,et al.  Agent intelligence through data mining , 2006, Multiagent systems, artificial societies, and simulated organizations.

[17]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[18]  Salvatore J. Stolfo,et al.  JAM: Java Agents for Meta-Learning over Distributed Databases , 1997, KDD.

[19]  Hillol Kargupta,et al.  Multi-agent Systems and Distributed Data Mining , 2004, CIA.

[20]  Lior Rokach,et al.  Top-down induction of decision trees classifiers - a survey , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[21]  Mathias Bauer,et al.  An ontology-based interface for machine learning , 2005, IUI '05.

[22]  Alexandros Kalousis,et al.  Algorithm selection via meta-learning , 2002 .

[23]  Calin Sandru,et al.  A Multi-Agent Problem Solving Architecture based on UPML , 2005, Artificial Intelligence and Applications.

[24]  Yong Cheng,et al.  MAGE: An Agent-Oriented Programming Environment , 2004 .

[25]  Melody Y. Kiang,et al.  A comparative assessment of classification methods , 2003, Decis. Support Syst..

[26]  Mario Cannataro,et al.  A Data Mining Ontology for Grid Programming , 2003 .

[27]  Martin L. Griss,et al.  Multi-agent cooperation, dynamic workflow and XML for e-commerce automation , 2000, AGENTS '00.

[28]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.