A Genetic Programming Approach to Feature Construction for Ensemble Learning in Skin Cancer Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Ain:2020:GECCO,

• author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and Mengjie Zhang",
• title = "A Genetic Programming Approach to Feature Construction for Ensemble Learning in Skin Cancer Detection",
• year = "2020",
• editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
• isbn13 = "9781450371285",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• URL = "https://doi.org/10.1145/3377930.3390228",
• DOI = "doi:10.1145/3377930.3390228",
• booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
• pages = "1186--1194",
• size = "9 pages",
• keywords = "genetic algorithms, genetic programming, ensemble classifiers, multi-class classification, melanoma detection, feature construction",
• address = "internet",
• series = "GECCO '20",
• month = jul # " 8-12",
• organisation = "SIGEVO",
• abstract = "Ensembles of classifiers have proved to be more effective than a single classification algorithm in skin image classification problems. Generally, the ensembles are created using the whole set of original features. However, some original features can be redundant and may not provide useful information in building good ensemble classifiers. To deal with this, existing feature construction methods that usually generate new features for only a single classifier have been developed but they fit the training data too well, resulting in poor test performance. This study develops a new classification method that combines feature construction and ensemble learning using genetic programming (GP) to address the above limitations. The proposed method is evaluated on two benchmark real-world skin image datasets. The experimental results reveal that the proposed algorithm has significantly outperformed two existing GP approaches, two state-of-the-art convolutional neural network methods, and ten commonly used machine learning algorithms. The evolved individual that is considered as a set of constructed features helps identify prominent original features which can assist dermatologists in making a diagnosis.",
• notes = "Also known as \cite{10.1145/3377930.3390228} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Qurrat Ul Ain Harith Al-Sahaf Bing Xue Mengjie Zhang

Citations