In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confidence is that it does not capture the correlation that exists between the left-hand side (LHS) and the right-hand side (RHS) of an association rule. On the other hand, the interestingness measure lift captures such as correlation in the sense that it tells us whether the LHS influences the RHS positively or negatively. Therefore, using Lift instead of confidence as a criteria for discovering association rules can be more effective. It also gives the user more choices in determining the kind of association rules to be discovered. This in turn helps to narrow down the search space and consequently, improves performance. In this paper, we describe a new approach for discovering association rules that is based on Lift and not based on confidence.
Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.