Metalearning: a survey of trends and technologies

Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.

[1]  Raghu Ramakrishnan,et al.  Proceedings : KDD 2000 : the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 20-23, 2000, Boston, MA, USA , 2000 .

[2]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection , 2012, ICCSA.

[3]  Hilan Bensusan,et al.  A Higher-order Approach to Meta-learning , 2000, ILP Work-in-progress reports.

[4]  John R. Rice,et al.  The Algorithm Selection Problem , 1976, Adv. Comput..

[5]  Johannes Fürnkranz,et al.  On the Use of Fast Subsampling Estimates for Algorithm Recommendation , 2002 .

[6]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[7]  Saso Dzeroski,et al.  Ranking with Predictive Clustering Trees , 2002, ECML.

[8]  Xiaozhe Wang,et al.  Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series , 2009, Neurocomputing.

[9]  Daniel L. Silver,et al.  Guest editor’s introduction: special issue on inductive transfer learning , 2008, Machine Learning.

[10]  Bogdan Gabrys,et al.  Meta-learning for time series forecasting and forecast combination , 2010, Neurocomputing.

[11]  Rüdiger Wirth,et al.  Towards Process-Oriented Tool Support for Knowledge Discovery in Databases , 1997, PKDD.

[12]  Ricardo Vilalta,et al.  Metalearning - Applications to Data Mining , 2008, Cognitive Technologies.

[13]  Carlos Soares,et al.  Report on the Experiments with Feature Selection in Meta-Level Learning , 2000 .

[14]  Antonio F. Gómez-Skarmeta,et al.  METALA: a J2EE Technology Based Framework for Web Mining , 2004, Rev. Colomb. de Computación.

[15]  Xin Yao,et al.  Evolving artificial neural network ensembles , 2008, IEEE Computational Intelligence Magazine.

[16]  Yiming Yang,et al.  Flexible latent variable models for multi-task learning , 2008, Machine Learning.

[17]  Carlos Soares.  UCI++: Improved Support for Algorithm Selection Using Datasetoids , 2009, PAKDD.

[18]  Ricardo Vilalta,et al.  A Characterization of Difficult Problems in Classification , 2002, ICMLA.

[19]  Joaquin Vanschoren.  Understanding Machine Learning Performance with Experiment Databases (Het verwerven van inzichten in leerperformantie met experiment databanken) ; Understanding Machine Learning Performance with Experiment Databases , 2010 .

[20]  Ivan Bruha,et al.  Postprocessing in machine learning and data mining , 2000, SKDD.

[21]  Melanie Hilario,et al.  Feature Selection for Meta-learning , 2001, PAKDD.

[22]  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.

[23]  Alexandros Kalousis,et al.  NOEMON: Design, implementation and performance results of an intelligent assistant for classifier selection , 1999, Intell. Data Anal..

[24]  Kurt Driessens,et al.  Meta-learning from an experiment database , 2012 .

[25]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[26]  Bogdan Gabrys,et al.  Learnt Topology Gating Artificial Neural Networks , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[27]  João Gama,et al.  Cascade Generalization , 2000, Machine Learning.

[28]  Ioannis Iglezakis,et al.  Combination of Task Description Strategies and Case Base Properties for Meta-Learning , 2002 .

[29]  Carlos Soares,et al.  Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results , 2003, Machine Learning.

[30]  Longbing Cao,et al.  Domain-Driven Data Mining: Challenges and Prospects , 2010, IEEE Transactions on Knowledge and Data Engineering.

[31]  Hendrik Blockeel,et al.  Towards understanding learning behavior , 2006 .

[32]  Norbert Jankowski,et al.  Universal Meta-Learning Architecture and Algorithms , 2011, Meta-Learning in Computational Intelligence.

[33]  Fabio Aiolli,et al.  Transfer Learning by Kernel Meta-Learning , 2011, ICML Unsupervised and Transfer Learning.

[34]  Bogdan Gabrys,et al.  Dynamic combination of forecasts generated by diversification procedures applied to forecasting of airline cancellations , 2009, 2009 IEEE Symposium on Computational Intelligence for Financial Engineering.

[35]  F. Shafait,et al.  Dataset Generation for Meta-Learning , 2012 .

[36]  Teresa Bernarda Ludermir,et al.  Meta-learning approaches to selecting time series models , 2004, Neurocomputing.

[37]  Piero P. Bonissone,et al.  Lazy Meta-Learning: Creating Customized Model Ensembles on Demand , 2012, WCCI.

[38]  Johan A. K. Suykens,et al.  Load forecasting using a multivariate meta-learning system , 2013, Expert Syst. Appl..

[39]  Charles A. Micchelli,et al.  Learning Multiple Tasks with Kernel Methods , 2005, J. Mach. Learn. Res..

[40]  Kate Smith-Miles,et al.  Cross-disciplinary perspectives on meta-learning for algorithm selection , 2009, CSUR.

[41]  Bogdan Gabrys,et al.  Ridge regression ensemble for toxicity prediction , 2010, ICCS.

[42]  Christophe G. Giraud-Carrier,et al.  The data mining advisor: meta-learning at the service of practitioners , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[43]  Norbert Jankowski,et al.  Meta-Learning in Computational Intelligence , 2013, Meta-Learning in Computational Intelligence.

[44]  Pierre Sens,et al.  Stream Processing of Healthcare Sensor Data: Studying User Traces to Identify Challenges from a Big Data Perspective , 2015, ANT/SEIT.

[45]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[46]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Combining meta-learning and search techniques to select parameters for support vector machines , 2012, Neurocomputing.

[47]  Andreas Dengel,et al.  Automatic classifier selection for non-experts , 2012, Pattern Analysis and Applications.

[48]  R. Geoff Dromey,et al.  An algorithm for the selection problem , 1986, Softw. Pract. Exp..

[49]  Christophe G. Giraud-Carrier.  Metalearning - A Tutorial , 2008 .

[50]  Albert Bifet,et al.  DATA STREAM MINING A Practical Approach , 2009 .

[51]  Felix Naumann,et al.  Data fusion , 2009, CSUR.

[52]  Melanie Hilario,et al.  Experimental Evaluation of the e-LICO Meta-Miner ( Extended Abstract ) , 2012 .

[53]  Ricardo Vilalta,et al.  A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.

[54]  Teresa Bernarda Ludermir,et al.  Using Machine Learning Techniques to Combine Forecasting Methods , 2004, Australian Conference on Artificial Intelligence.

[55]  Bogdan Gabrys,et al.  Architecture for development of adaptive on-line prediction models , 2009, Memetic Comput..

[56]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[57]  Sebastián Ventura,et al.  Meta-learning Approach for Automatic Parameter Tuning: A case of study with educational datasets , 2012, EDM.

[58]  Saso Dzeroski,et al.  Combining Classifiers with Meta Decision Trees , 2003, Machine Learning.

[59]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[60]  Daniel L. Silver,et al.  Inductive transfer with context-sensitive neural networks , 2008, Machine Learning.

[61]  Graham J. Williams,et al.  PMML: An Open Standard for Sharing Models , 2009, R J..

[62]  Ahmed Abbasi,et al.  MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud , 2012, MIS Q..

[63]  Hilan Bensusan,et al.  Meta-Learning by Landmarking Various Learning Algorithms , 2000, ICML.

[64]  B. Gabrys,et al.  Robust predictive modelling of water pollution using biomarker data. , 2010, Water research.

[65]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[66]  J. Biggs.  THE ROLE OF METALEARNING IN STUDY PROCESSES , 1985 .

[67]  Peter A. Flach,et al.  PKDD2000 workshop on Data Mining, Decision Support, Meta-learning and ILP : Forum for Practical Problem Presentation and Prospective Solutions , 2000 .

[68]  Norbert Jankowski,et al.  Complexity Measures for Meta-learning and Their Optimality , 2011, Algorithmic Probability and Friends.

[69]  Andreas Dengel,et al.  Meta-learning for evolutionary parameter optimization of classifiers , 2012, Machine Learning.

[70]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[71]  Chih-Fong Tsai,et al.  A Meta‐learning Framework for Bankruptcy Prediction , 2013 .

[72]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[73]  Carlos Soares,et al.  A Comparison of Ranking Methods for Classification Algorithm Selection , 2000, ECML.

[74]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[75]  Katharina Morik,et al.  The MiningMart Approach to Knowledge Discovery in Databases , 2004 .