Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

The support vector machine (SVM) has provided higher performance than traditional learning machines and has been widely applied in real-world classification problems and nonlinear function estimation problems. Unfortunately, the training process of the SVM is sensitive to the outliers or noises in the training set. In this paper, a common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises. In the KFCM-FSVM algorithm, we first use the FCM clustering to cluster each of two classes from the training set in the high-dimensional feature space. The farthest pair of clusters, where one cluster comes from the positive class and the other from the negative class, is then searched and forms one new training set with membership degrees. Finally, we adopt FSVM to induce the final classification results on this new training set. The computational complexity of the KFCM-FSVM algorithm is analyzed. A set of experiments is conducted on six benchmarking datasets and four artificial datasets for testing the generalization performance of the KFCM-FSVM algorithm. The results indicate that the KFCM-FSVM algorithm is robust for classification problems with outliers or noises.