A System for Accessible Artificial Intelligence

Olson, Randal S.; Sipper, Moshe; Cava, William La; Tartarone, Sharon; Vitale, Steven; Fu, Weixuan; Orzechowski, Patryk; Urbanowicz, Ryan J.; Holmes, John H.; Moore, Jason H.

doi:10.1007/978-3-319-90512-9_8

Cited by 18 publications

(15 citation statements)

References 27 publications

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“…It seems natural to ask whether the myriad parameters can be obtained through some clever methodology (perhaps even an evolutionary one) rather than by trial and error; indeed, as we shall see below, such methods have been previously devised. Our own interest in the issue of parameters stems partly from a desire to better understand evolutionary algorithms (EAs) and partly from our recent investigation into the design and implementation of an accessible artificial intelligence system [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating the parameter space of evolutionary algorithms

Sipper

Ahuja

et al. 2018

BioData Mining

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Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.

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Section: Introductionmentioning

confidence: 99%

Investigating the parameter space of evolutionary algorithms

Sipper

Ahuja

et al. 2018

BioData Mining

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“…For a more nuanced approach, the similarity of the dataset on which ML is to be applied to datasets in PMLB could be quantified, and the set of algorithms that performed best on those similar datasets could be used. In lieu of detailed problem information, one could also use automated ML tools 16,17 and AI-driven ML platforms 18 to perform model selection and parameter tuning automatically.…”

Section: Discussionmentioning

confidence: 99%

Data-driven advice for applying machine learning to bioinformatics problems

et al. 2017

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and jhmoore@upenn.eduAs the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems.

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“…Alternatively, you can consider taking advantage of some automatic machine learning software methods, which automatically optimize the hyper-parameters of the algorithm you selected. These packages include Auto-Sklearn [ 35 ], Auto-Weka [ 36 ], TPOT [ 37 ], and PennAI [ 38 ].…”

Section: Tip 6: Optimize Each Hyper-parametermentioning

confidence: 99%

Ten quick tips for machine learning in computational biology

2017

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Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

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A System for Accessible Artificial Intelligence

Cited by 18 publications

References 27 publications

Investigating the parameter space of evolutionary algorithms

Investigating the parameter space of evolutionary algorithms

Data-driven advice for applying machine learning to bioinformatics problems

Ten quick tips for machine learning in computational biology

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