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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.

Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.

Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with large amounts of data has received a lot of attention in the field recently, and a number of methodologies have been proposed to extract insights from data in an automated or semi-automated manner. However, these methodologies generally target a specific aspect of the data mining process, such as data acquisition, data preprocessing, or data classification. However, a comprehensive knowledge acquisition method is crucial to support the end-to-end knowledge engineering process. In this paper, we introduce a knowledge acquisition system that covers all major phases of the cross-industry standard process for data mining. Acknowledging the importance of an end-to-end knowledge engineering process, we designed and developed an easy-to-use data-driven knowledge acquisition tool (DDKAT). The major features of the DDKAT are: (1) a novel unified features scoring approach for data selection; (2) a user-friendly data processing interface to improve the quality of the raw data; (3) an appropriate decision tree algorithm selection approach to build a classification model; and (4) the generation of production rules from various decision tree classification models in an automated manner. Furthermore, two diabetes studies were performed to assess the value of the DDKAT in terms of user experience. A total of 19 experts were involved in the first study and 102 students in the artificial intelligence domain were involved in the second study. The results showed that the overall user experience of the DDKAT was positive in terms of its attractiveness, as well as its pragmatic and hedonic quality factors.