Learning in a Fuzzy Random Forest ensemble from imperfect data

Instrument errors or noise interference during experiments may lead to incomplete data when measuring a specific attribute. Obtaining models from imperfect data is a topic currently being treated with more interest. In this paper, we present the learning phase of a Fuzzy Random Forest ensemble for classification from imperfect data. We perform experiments with imperfect datasets created for this purpose and datasets used in other papers to show the express the true nature of imperfect information.

[1]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  W. Marsden.  I and J , 2012 .

[4]  L. Goddard.  Information Theory , 1962, Nature.

[5]  M. C. Garrido,et al.  Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians , 1998, J. Artif. Intell. Res..

[6]  Piero P. Bonissone,et al.  Approximate Reasoning Systems: Handling Uncertainty and Imprecision in Information Systems , 1996, Uncertainty Management in Information Systems.

[7]  A. Otero,et al.  Longest path estimation from inherently fuzzy data acquired with GPS using genetic algorithms , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[8]  Cezary Z. Janikow,et al.  Fuzzy decision trees: issues and methods , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[10]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[11]  Inés Couso,et al.  Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets , 2009, Evol. Intell..

[12]  Piero P. Bonissone,et al.  A fuzzy random forest , 2010, Int. J. Approx. Reason..

[13]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[14]  Piero P. Bonissone,et al.  A classification and regression technique to handle heterogeneous and imperfect information , 2010, Soft Comput..

[15]  Francisco Herrera,et al.  A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability , 2009, Soft Comput..