Collaborative Analytics with Genetic Programming for Workflow Recommendation

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

@InProceedings{Chong:2013:SMC,

• author = "Chee Seng Chong and Tianyou Zhang and Kee Khoon Lee and Gih Guang Hung and Bu-Sung Lee",
• title = "Collaborative Analytics with Genetic Programming for Workflow Recommendation",
• booktitle = "IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013)",
• year = "2013",
• month = oct,
• pages = "657--662",
• keywords = "genetic algorithms, genetic programming, Work flow recommendation, collaborative analytics",
• DOI = "doi:10.1109/SMC.2013.117",
• size = "6 pages",
• abstract = "Formulation of appropriate data analytics workflows requires intricate knowledge and rich experiences of data analytics experts. This problem is further compounded by continuous advancement and improvement in analytical algorithms. In this paper, a generic non-domain specific solution for the creation of appropriate work-flows targeted at supervised learning problems is proposed. Our adaptive work flow recommendation engine based on collaborative analytics matches analytics needs with relevant work flows in repository. It is capable of picking workflows with better performance as compared to randomly selected work-flows. The recommendation engine is now augmented by a work-flow optimiser that applies genetic programming to further improve the recommended workflows through iterative evolution, leading to better alternative workflows. This unique Collaborative Analytics Recommender System is tested on seven UCI benchmark datasets. It is shown that the final workflows produced by the system could closely approximate, in terms of accuracy, the best workflows that analytics experts could possibly design.",
• notes = "Terence Hung = Gih Guang Hung. Bu-Sung Lee = Francis Lee. Also known as \cite{6721870}",

}

Genetic Programming entries for Chee Seng Chong Tianyou Zhang Kee Khoon Lee Terence Hung Bu Sung Lee

Citations