A Genetic Programming Approach Applied to Feature Selection from Medical Data

Genetic programming represents a flexible and powerful evolutionary technique in machine learning. The use of genetic programming for rule induction has generated interesting results in classification problems. This paper proposes an evolutionary approach for logical rule induction, which is applied to clinical data. Since logical rules disclose knowledge from the analyzed data, we use such a knowledge to filter features from the target dataset. The results reached by the used dataset have been very promising when used in classification tasks and compared with other methods.

[1]  Peter A. Flach,et al.  Machine Learning - The Art and Science of Algorithms that Make Sense of Data , 2012 .

[2]  Ida G. Sprinkhuizen-Kuyper,et al.  Unanimous Voting using Support Vector Machines , 2005 .

[3]  Francisco Herrera,et al.  Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study , 2010, IEEE Transactions on Evolutionary Computation.

[4]  Concha Bielza,et al.  Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.

[5]  Marko Robnik-Sikonja,et al.  Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.

[6]  Gisele L. Pappa,et al.  Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach , 2009 .

[7]  Jinyan Li,et al.  Using Rules to Analyse Bio-medical Data: A Comparison between C4.5 and PCL , 2003, WAIM.

[8]  Alexandre Parodi,et al.  An Efficient Classifier System and Its Experimental Comparison with Two Representative Learning Methods on Three Medical Domains , 1991, ICGA.

[9]  Vili Podgorelec,et al.  Knowledge discovery with classification rules in a cardiovascular dataset , 2005, Comput. Methods Programs Biomed..

[10]  Sung-Bae Cho,et al.  The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming , 2006, Artif. Intell. Medicine.

[11]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[12]  Gisele L. Pappa,et al.  Automating the Design of Data Mining Algorithms , 2010 .

[13]  Peter J. F. Lucas,et al.  Analysis of Notions of Diagnosis , 1998, Artif. Intell..

[14]  Alex Alves Freitas,et al.  Evolving rule induction algorithms with multi-objective grammar-based genetic programming , 2009, Knowledge and Information Systems.

[15]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[16]  Zhi-Hua Zhou,et al.  NeC4.5: Neural Ensemble Based C4.5 , 2004, IEEE Trans. Knowl. Data Eng..

[17]  O. Mangasarian,et al.  Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Alex A. Freitas,et al.  A survey of evolutionary algorithms for data mining and knowledge discovery , 2003 .

[19]  Georgios Dounias,et al.  Evolving rule-based systems in two medical domains using genetic programming , 2004, Artif. Intell. Medicine.

[20]  Juan Francisco de Paz,et al.  A Clustering-Based Method for Gene Selection to Classify Tissue Samples in Lung Cancer , 2016, PACBB.

[21]  Hitoshi Iba,et al.  Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data , 2009, TCBB.

[22]  Alfonso González-Briones,et al.  An Agent-Based Clustering Approach for Gene Selection in Gene Expression Microarray , 2017, Interdisciplinary Sciences: Computational Life Sciences.

[23]  Moshe Sipper,et al.  Evolutionary computation in medicine: an overview , 2000, Artif. Intell. Medicine.

[24]  Martin V. Butz,et al.  Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule , 2005, IWLCS.

[25]  Juan F. De Paz,et al.  A drug identification system for intoxicated drivers based on a systematic review , 2016 .

[26]  Ujjwal Maulik,et al.  Multiobjective Genetic Algorithms for Clustering - Applications in Data Mining and Bioinformatics , 2011 .

[27]  Francisco Herrera,et al.  A Survey on the Application of Genetic Programming to Classification , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[28]  A Abu-Hanna,et al.  Prognostic methods in medicine. , 1999, Artificial intelligence in medicine.

[29]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[30]  José Antonio Castellanos Garzón,et al.  A Gene Selection Approach based on Clustering for Classification Tasks in Colon Cancer , 2016 .

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  Josiah Adeyemo,et al.  Genetic Programming: Principles, Applications and Opportunities for Hydrological Modelling , 2014 .

[33]  Alex Alves Freitas.  A Review of evolutionary Algorithms for Data Mining , 2008, Soft Computing for Knowledge Discovery and Data Mining.

[34]  Chun-Gui Xu,et al.  A genetic programming-based approach to the classification of multiclass microarray datasets , 2009, Bioinform..