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

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

/pdf/smote-for-learning-from-imbalanced-data-progress-and-3slcu62yo6.pdf

SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

Learning from class-imbalanced data: Review of methods and applications

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Applied Multivariate Techniques

Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

Progress in Outlier Detection Techniques: A Survey

The class imbalance problem is frequently found in many real-world domains, where many of traditional classifiers often fail to detect minority class objects due to paying less attention to those. In an effort to address this class imbalance problem, a new under-sampling technique GAUS (genetic algorithm based under-sampling) is proposed in this paper. GAUS is designed to overcome several limitations of existing methods such as performance instability and information loss of data distribution. To select informative majority objects, GAUS tries to maximize the performance of a prototype classifier such that the prototypes minimize the loss between distributions of original and undersampled majority objects. We confirmed the effectiveness of the proposed GAUS based on real-world datasets.

A New Under-Sampling Method Using Genetic Algorithm for Imbalanced Data Classification

With the growing interest in skincare and maintenance, there are increasing numbers of studies on the classification of skin type and the factors influencing each type. This study presents a novel methodology by using data mining, for the determination of the relationships between skin type, lifestyle, and patterns of cosmetic utilization. Eight skin-specific factors, which are moisture, sebum in U-zone (both cheeks), sebum in T-zone (forehead, nose, and chin), pore, melanin, wrinkle, acne, hemoglobin, were measured in 1,246 subjects living in South Korea, in conjunction with a questionnaire survey analyzing their lifestyles and pattern of cosmetic utilization. Using various multivariate statistical methods and data mining techniques, we classified the skin types based on the skin-specific values, determined the relationship between skin type and lifestyle, and accordingly sorted the subjects into clusters. Logistic regression analysis revealed gender-related differences in the skin; therefore, separate analyses were performed for males and females. Using the Gaussian Mixture Modeling (GMM) technique, we classified the subjects based on skin type (two male and four female). Using the ANOVA and decision tree techniques, we attempted to characterize the relationship between each skin type and the lifestyles of the subjects. Menstruation, eating habits, stress, and smoking were identified as the major factors affecting the skin.

https://www.koreascience.or.kr:443/article/JAKO201612461130939.pdf

Discovering Relationships between Skin Type and Life Style Using Data Mining Techniques: A Case Study of Korea

Since outlier detection is applicable to various fields such as the financial, telecommunications, medical, and commercial industries, its importance is radically increasing. Receiving such great attention has led to the development of many detection methods, most of which pertain to either the distance-based approach or the density-based approach. However, each approach has intrinsic weaknesses. The former hardly detects local outliers, while the latter has the low density patterns problem. To overcome these weaknesses, we proposed a new detection method that introduces the instability factor of a data point by utilizing the concept of the center of gravity. The proposed method can be flexibly used for both local and global detection of outliers by controlling its parameter. In addition, it offers the instability plot containing useful information about the number and size of clusters in data. Numerical experiments based on artificial and real datasets show the effectiveness of the proposed method.

Robust outlier detection using the instability factor

In order to handle the class imbalance problem, synthetic data generation methods such as SMOTE, ADASYN, and Borderline-SMOTE have been developed. These methods use a common parameter k, the number of nearest neighbors. Nonetheless the most effective k value depends on the given dataset, there is no guideline to determine k. Moreover, if a dataset has noises, small sub-clusters, or complex patterns, the existing SMOTE and its variants show poor classification performance. Our method that we named Automatic Neighborhood size Determination(AND) restricts the size of neighborhood in SMOTE to maintain the original distribution of data, and helps SMOTE for its best performance. Defining and examining a minority region including a minority class object and its neighbors assure safety for generating synthetic samples. The proposed AND-method determines the value of k for each minority objects. By independently generating synthetic minority samples with the automatically predefined k values, we aim at achieving a better classification than the existing methods. Numerical experiments showed that the proposed method outperformed SMOTE, ADASYN, or Borderline-SMOTE.

Automatic Determination of Neighborhood Size in SMOTE

A precise ranking method for outlier detection

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