论文信息 - Enhanced confidence interpretations of GP based ensemble modeling results

Enhanced confidence interpretations of GP based ensemble modeling results

In this paper we describe the integration of ensemble modeling into genetic programming based classification and discuss concepts how to use genetic programming specific features for achieving new confidence indicators that estimate the trustworthiness of predictions. These new concepts are tested on a real world dataset from the field of medical diagnosis for cancer prediction where the trustworthiness of modeling results is of highest importance.

[1] Leo Breiman,et al. Random Forests , 2001, Machine Learning.

[2] Vladan Babovic,et al. Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff - Introductory Investigations , 2000, EuroGP.

[3] Stephan M. Winkler,et al. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications , 2009 .

[4] Witold Jacak,et al. Classification of tumor marker values using heuristic data mining methods , 2010, GECCO '10.

[5] Witold Jacak,et al. Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system , 2011, GECCO.