Research into machine learning methods for fraud detection is of paramount importance, largely due to the substantial financial implications associated with fraudulent activities. Our investigation is centered around the Credit Card Fraud Dataset and the Medicare Part D dataset, both of which are highly imbalanced. The Credit Card Fraud Detection Dataset is large data and contains actual transactional content, which makes it an ideal benchmark for credit card fraud detection. The Medicare Part D dataset is big data, providing researchers the opportunity to examine national trends and patterns related to prescription drug usage and expenditures. This paper presents a detailed comparison of One-Class Classification (OCC) and binary classification algorithms, utilizing eight distinct classifiers. OCC is a more appealing option, since collecting a second label for binary classification can be very expensive and not possible to obtain within a reasonable time frame. We evaluate our models based on two key metrics: the Area Under the Precision-Recall Curve (AUPRC)) and the Area Under the Receiver Operating Characteristic Curve (AUC). Our results show that binary classification consistently outperforms OCC in detecting fraud within both datasets. In addition, we found that CatBoost is the most performant among the classifiers tested. Moreover, we contribute novel results by being the first to publish a performance comparison of OCC and binary classification specifically for fraud detection in the Credit Card Fraud and Medicare Part D datasets.

Our study evaluates the impact of training OneClass Classification (OCC) algorithms on the majority class compared to training them on the minority class, using large and big datasets that are highly imbalanced. It is important to note that class availability can present a significant challenge in model training. In reality, there may be situations where one class is readily obtainable within a reasonable time frame while another class is not. Our task involves detecting instances of fraud in the Credit Fraud Detection Dataset and our Medicare dataset derived from Medicare Part D data and List of Excluded Individuals and Entities (LEIE) data. The Credit Card Fraud Detection Dataset has real-world transaction content as well as a significant class imbalance, making it suitable for use as a benchmark for credit card fraud detection. In addition, it is the only publicly available large data for credit card fraud analysis. Part D is big data, allowing researchers to analyze national trends and patterns in prescription drug usage and expenditures. The algorithms used in the study are One-Class Gaussian Mixture Model (GMM), OneClass Adversarial Nets (OCAN), and One-Class Support Vector Machine (SVM). Their performance is measured with the Area Under the Precision-Recall Curve (AUPRC) and Area Under the Receiver Operating Characteristic Curve (AUC). Our results indicate that OCC produces better results when models are trained on the majority class.

In severely imbalanced datasets, using traditional binary or multi-class classification typically leads to bias towards the class(es) with the much larger number of instances. Under such conditions, modeling and detecting instances of the minority class is very difficult. One-class classification (OCC) is an approach to detect abnormal data points compared to the instances of the known class and can serve to address issues related to severely imbalanced datasets, which are especially very common in big data. We present a detailed survey of OCC-related literature works published over the last decade, approximately. We group the different works into three categories: outlier detection, novelty detection, and deep learning and OCC. We closely examine and evaluate selected works on OCC such that a good cross section of approaches, methods, and application domains is represented in the survey. Commonly used techniques in OCC for outlier detection and for novelty detection, respectively, are discussed. We observed one area that has been largely omitted in OCC-related literature is its application context for big data and its inherently associated problems, such as severe class imbalance, class rarity, noisy data, feature selection, and data reduction. We feel the survey will be appreciated by researchers working in these areas of big data.

The second most prevalent age-related neurodegenerative disease is Parkinson's (PD) and Genes associated with human diseases like Parkinson are descriptive. Genome-wide association study (GWAS) is used to classify the genes associated with Parkinson’s and other diseases. The information of identified genes empowers scientists to early diagnose, treat, and sop diseases. Due to the complexities of the illness, identifying such genes is a challenging task. In this article, we apply two methods of feature selection to choose a subset of genes that are used to predict PD with high precision in classification. The chromosome corresponding to selected features is analyzed by Perturbation-based Feature Selection (PFS) and Hilbert-Schmidt independence criterion (HSIC)-Lasso. These algorithms are used to identify how chromosomes play an important role with respect to PD. We used a dataset consist of 50 predominantly patients gene expression profiles with early-stage Parkinson's disease (PD) and 55 normal GEO samples. These methods provide a series of features involved in disease-specific processes that are applied to prioritize candidate genes in GWAS loci.

Nowadays, distributed systems are prevalent and practical in network environments. In distributed systems, pattern recognition help to extract information from network nodes. Meanwhile, data mining in such systems needs resource consideration in terms of storage and computational time. The primary requirement of these systems is a scalable mechanism to distribute the tasks on several databases. Moreover, to do a centralized process, relocating data from all nodes or partial nodes to a central node has confidential risks and traffic overhead. Therefore, distributed data mining in distributed environments needs systematic and structural techniques. In this paper, we propose a new algorithm to extract frequent itemsets in Wireless Sensor Networks. Through this algorithm, nodes frequent local itemsets are obtained with a Bitwise approach, and nodes are classified into clusters by using the Low Energy-Adaptive Clustering Hierarchy (LEACH) algorithm. Connecting the head cluster is performed by a Gossip-based protocol to achieve the values of global support, and it finally resulted in the extraction of frequent itemsets. The proposed algorithm has been simulated in various scenarios using Java software, and algorithm efficiency is evaluated in terms of execution time and average accuracy. Our algorithm is compared with a Gossip-based algorithm, and then some improvements in execution time have been presented.