Abstract
Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction.
Highlights
Classification is the most widely studied machine learning paradigm
The drawbacks of conventional approaches for algorithm selection and the continuous successful applications of classification algorithms in numerous domains, there has been an ever-growing demand of machine learning systems that can automate the process of algorithm selection, i.e. provide intelligent assistance to end users by recommending potentially appropriate algorithms for various different tasks [7], [8]
In this work, we have deeply analyzed the problem of metalearning based classification algorithm recommendation
Summary
Classification is the most widely studied machine learning paradigm. The standard approach of classification algorithms is to learn from labeled training examples and use that learning for classification of new unseen instances of dataset. The drawbacks of conventional approaches for algorithm selection and the continuous successful applications of classification algorithms in numerous domains, there has been an ever-growing demand of machine learning systems that can automate the process of algorithm selection, i.e. provide intelligent assistance to end users by recommending potentially appropriate algorithms for various different tasks [7], [8]. It overcome the limitations of conventional algorithm selection approaches by automating the process of algorithm selection Domain experts use their knowledge regarding performance of machine learning algorithms on previous tasks.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
