2 research outputs found
Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
Generative Adversarial Networks (GANs) are popular tools for generative
modeling. The dynamics of their adversarial learning give rise to convergence
pathologies during training such as mode and discriminator collapse. In machine
learning, ensembles of predictors demonstrate better results than a single
predictor for many tasks. In this study, we apply two evolutionary algorithms
(EAs) to create ensembles to re-purpose generative models, i.e., given a set of
heterogeneous generators that were optimized for one objective (e.g., minimize
Frechet Inception Distance), create ensembles of them for optimizing a
different objective (e.g., maximize the diversity of the generated samples).
The first method is restricted by the exact size of the ensemble and the second
method only restricts the upper bound of the ensemble size. Experimental
analysis on the MNIST image benchmark demonstrates that both EA ensembles
creation methods can re-purpose the models, without reducing their original
functionality. The EA-based demonstrate significantly better performance
compared to other heuristic-based methods. When comparing both evolutionary,
the one with only an upper size bound on the ensemble size is the best.Comment: Accepted as a full paper for the Genetic and Evolutionary Computation
Conference - GECCO'2
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