527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

Motivation: Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method. Results: Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-ofthe-art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray data sets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of about 15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature.

Robust biomarker identification for cancer diagnosis using ensemble feature selection methods

Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

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A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection.

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983–2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

A Systematic Review on Educational Data Mining

The invention of DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Although this technology has shifted a new era in molecular classification, interpreting microarray data still remain a challenging issue due to their innate nature of “high dimensional low sample size”. Therefore, robust and accurate feature selection methods are required to identify differentially expressed genes across varied samples for example between cancerous and normal cells. Successful of feature selection techniques will assist to correctly classify different cancer types and consequently led to a better understanding of genetic signatures in cancers and would improve treatment strategies. This paper presents a review of feature selection techniques that have been employed in microarray data analysis. Moreover, other problems associated with microarray data analysis also addressed. In addition, several trends were noted including highly reliance on filter techniques compared to wrapper and embedded, a growing direction towards ensemble feature selection techniques and future extension to apply feature selection in combination of heterogeneous data sources.

A review of feature selection techniques via gene expression profiles

The Internet of Things (IoT) is the interconnection of uniquely identifiable embedded computing devices within the existing Internet infrastructure. Typically, internet of things (IoT) is expected to offer advanced connectivity of devices, systems, and services that goes beyond machine-to-machine communications (M2M) and covers a variety of protocols, domains, and applications. The interconnection of these embedded devices including smart objects, is expected to usher in automation in nearly all fields, while also enabling advanced applications like a Smart Grid. The main research challenge in Internet of things (IoT) for the forensic investigators is based size of the objects of forensic interest, relevancy, blurry network boundaries and edgeless networks, especially on method for conducting the investigation. The aim of this paper is to identify the best approach by designing a novel model to conduct the investigation situations for digital forensic professionals and experts. There was existing research works which introduce models for identifying the objects of forensics interest in investigations, but there were no rigorous testing for accepting the approach. Currently in this work, an integrated model is designed based on triage model and 1-2-3 zone model for volatile based data preservation.

Internet of Things(IoT) digital forensic investigation model: Top-down forensic approach methodology

Purpose – The purpose of this paper is to present the utilization of a web-based support system for flood response operation in Malaysia The system is intentionally designed to improve process and data management towards providing prompt and effective response to victims
Design/methodology/approach – The implementation of the research adopted the knowledge acquisition method for implementing expert systems in organizations The methodology consists of four phases of activities which are planning for knowledge acquisition, knowledge extraction, knowledge analysis, and knowledge verification
Findings – The findings include the architecture of the web-based support system for flood response operation which is presented in the form of conceptual and software models The architecture of web-based support systems for flood response operation can assist the flood management related
agencies in managing and maintaining data related to floods It also allows them to monitor the current situation of flood-related mattersPractical implications – Web-based support systems for flood response operation is expected to improve the overall aspect of flood response operation by providing electronic features which facilitate
the flood response process and data management In future, expert systems for flood response operations is one of the enhancements to the current architectureOriginality/value – This paper presents the architecture of a web-based support system for flood response operation in Malaysia The proposed architecture is beneficial for the flood management related agencies in order to plan further improvements in the current procedure for flood response

Web‐based support system for flood response operation in Malaysia

In recent years, web security has been viewed in the context of securing the web application layer from attacks by unauthorized users. The vulnerabilities existing in the web application layer have been attributed either to using an inappropriate software development model to guide the development process, or the use of a software development model that does not consider security as a key factor. Therefore, this systematic literature review is conducted to investigate the various security development models used to secure the web application layer, the security approaches or techniques used in the process, the stages in the development model in which the approaches or techniques are emphasized, and the tools and mechanism used to detect vulnerabilities. The study extracted 499 publications from respectable scientific sources, i.e. the IEEE Computer Society, ACM Digital Library, Google-Scholar, Science Direct, Scopus, Springer Link and ISI Web. After investigation, only 43 key primary studies were considered for this review based on defined inclusion and exclusion criteria. From the review, it appears that no one development model is referred to as a standard or preferred model for web application development. However, agile development models seem to have gained more attention, probably due to the multiple stakeholders that are involved in discussing security viewpoints, rather than a few members of the development team. It appears also that there is consistency in the use of the threat-modeling technique, probably due to its effectiveness in dealing with different kinds of vulnerabilities.

http://ddms.usim.edu.my:80/jspui/bitstream/123456789/8433/3/Systematic%20review%20of%20web%20application%20security%20development%20model.pdf

Systematic review of web application security development model

In Information Technology related programs, computer programming courses are made compulsory subjects in most institutions of learning. However there are many reports on poor performance among students in such courses. Previous studies examined some of the variables influencing students' performance using statistical data analysis. The critical point of this study is the use of classification algorithm to extract patterns which are examined from the cognitive factor specific learning style. The findings show that that student's good performance in programming courses has a visual, active and sequential learning style.

Norita Md Norwawi

Papers

A review of feature selection techniques via gene expression profiles

Internet of Things(IoT) digital forensic investigation model: Top-down forensic approach methodology

Web‐based support system for flood response operation in Malaysia

Systematic review of web application security development model

Classification of students' performance in computer programming course according to learning style