Expression of Concern: Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278 This Expression of Concern is for the above article, published online on August 2, 2018 in Wiley Online Library (wileyonlinelibrary.com), and has been published by agreement between the journal

Wiley received notification of concern about whether the ethical requirements for human subject research were met in a study by Wang, C. and colleagues that was published in WIREs Data Mining Knowledge Discovery (Wang et al., 2019) on August 2, 2018. Study participants are reported to be from ethnic or religious minority groups, with some considered part of a vulnerable population. Wiley and the WIREs

Healthcare 4.0 is a term that has emerged recently and derived from Industry 4.0. Today, the health care sector is more digital than in past decades; for example, spreading from x‐rays and magnetic resonance imaging to computed tomography and ultrasound scans to electric medical records. With the wide spectrum of digital technologies underpinning Healthcare 4.0 to deliver more effective and efficient

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, that is, comments. A news outlet may allow users to contribute comments on all their articles or a selected number of them. The topic of an article may lead to an apathetic user commenting activity (several tens of comments) or to a

Algorithmic differentiation (AD), also known as automatic differentiation, is a technology for accurate and efficient evaluation of derivatives of a function given as a computer model. The evaluations of such models are essential building blocks in numerous scientific computing and data analysis applications, including optimization, parameter identification, sensitivity analysis, uncertainty quantification

The increasing computational power and proliferation of big data are now empowering Artificial Intelligence (AI) to achieve massive adoption and applicability in many fields. The lack of explanation when it comes to the decisions made by today's AI algorithms is a major drawback in critical decision‐making systems. For example, deep learning does not offer control or reasoning over its internal processes

The ring main unit (RMU) is the main equipment of the electrical power distribution network. The deterioration process of the RMU cable joint is multivariable and nonstationary over time. Existing fault detection models for the RMU cable joint are single‐variable and static over time. A novel algorithm of adaptive time neighborhood preserving embedding (ATNPE) is proposed in this paper, a multivariable

Players in the telecommunications sector struggle against the competition to keep customers, and therefore they need effective churn management. Most classification algorithms either ignore misclassification cost or assume that the costs of all incorrect classification errors are equal. But as in real life, many classification problems have different misclassification costs and this difference cannot

In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For example, in clustering problems, the input is a set of points in some metric space, and a common goal is to compute a set of centers in some other space (points, lines) that will minimize

Social networks (SNs) represent an established environment in which users share daily emotions and opinions. Therefore, they have become an essential source of big data related to sentiment/opinion sphere. Sentiment analysis (SA) aims to extract sentiments, emotions or opinions from texts, made available by different data sources like SNs. This review presents a depth study relative to the methods

The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently

Sarcasm in verbal and nonverbal communication is known to attract higher attention and create deeper influence than other negative responses. Many people are adept at including sarcasm in written communication thus sarcastic comments have the potential to stimulate the virality of social media content. Although diverse computational approaches have been used to detect sarcasm in social media, the use

Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible for extracting frequently occurring events, patterns, or items in data. Insights from such pattern analysis offer important benefits in decision‐making processes. However, algorithmic solutions for mining such kind of patterns are not straightforward since the computational complexity exponentially increases

Sentiment discovery and analysis (SDA) aims to automatically identify the underlying attitudes, sentiments, and subjectivity towards a certain entity such as learners and learning resources. Due to its enormous potential for smart education, SDA has been deemed as a powerful technique for identifying and classifying sentiments from multimodal and multisource data over the whole process of education

With the increasing role of ICT in enabling and supporting smart cities, the demand for big data analytics solutions is increasing. Various artificial intelligence, data mining, machine learning and statistical analysis‐based solutions have been successfully applied in thematic domains like climate science, energy management, transport, air quality management and weather pattern analysis. In this paper

The article presents an overview of the status quo in benchmarking in classification and nonlinear regression. It outlines guidelines for a comparative analysis in machine learning, benchmarking principles, accuracy estimation, and model validation. It provides references to established repositories and competitions and discusses the objectives and limitations of benchmarking. Benchmarking is key to

Data classification and regression are commonly encountered data analysis problems. Many researchers created multiple tools to deal with these issues. Fuzzy clustering, fuzzy decision trees, and ensemble classifiers such as fuzzy forests are popular tools used for this kind of problems. We would like to describe some interesting, more or less popular, solutions which belong to mentioned areas to show

Natural heterogeneity in patient populations can make it very hard to develop treatments that benefit all patients. As a result, an important goal of precision medicine is identification of patient subgroups that respond to treatment at a much higher (or lower) rate than the population average. Despite there being many subgroup identification methods, there is no comprehensive comparative study of

Separable nonnegative matrix factorization (SNMF) is an important method for topic modeling, where “separable” assumes every topic contains at least one anchor word, defined as a word that has non‐zero probability only on that topic. SNMF focuses on the word co‐occurrence patterns to reveal topics by two steps: anchor word selection and topic recovery. The quality of the anchor words strongly influences

Sequential pattern mining (SPM) is a well‐studied theme in data mining, in which one aims to discover common sequences of item sets in a large corpus of temporal itemset data. Due to the sequential nature of data streams, supporting SPM in streaming environments is commonly studied in the area of data stream mining as well. On the other hand, stream‐based process discovery (PD), originating from the

Regardless of the marvels brought by the conventional frame‐based cameras, they have significant drawbacks due to their redundancy in data and temporal latency. This causes problem in applications where low‐latency transmission and high‐speed processing are mandatory. Proceeding along this line of thought, the neurobiological principles of the biological retina have been adapted to accomplish data