Gait-based Gender Classification Considering Resampling and Feature Selection

Two intrinsic data characteristics that arise in many domains are the class imbalance and the high dimensionality, which pose new challenges that should be addressed. When using gait for gender classification, benchmarking public databases and renowned gait representations lead to these two problems, but they have not been jointly studied in depth. This paper is a preliminary study that pursues to investigate the benefits of using several techniques to tackle the aforementioned problems either singly or in combination, and also to evaluate the order of application that leads to the best classification performance. Experimental results show the importance of jointly managing both problems for gait-based gender classification. In particular, it seems that the best strategy consists of applying resampling followed by feature selection

Survey on highly imbalanced multi-class data

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multi-class data