FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid

Protecting data privacy is paramount in the fields such as finance, banking, and healthcare. Federated Learning (FL) has attracted widespread attention due to its decentralized, distributed training and the ability to protect the privacy while obtaining a global shared model. However, FL presents challenges such as communication overhead, and limited resource capability. This motivated us to propose a two-stage federated learning approach toward the objective of privacy protection, which is a first-of-its-kind study as follows: (i) During the first stage, the synthetic dataset is generated by employing two different distributions as noise to the vanilla conditional tabular generative adversarial neural network (CTGAN) resulting in modified CTGAN, and (ii) In the second stage, the Federated Probabilistic Neural Network (FedPNN) is developed and employed for building globally shared classification model. We also employed synthetic dataset metrics to check the quality of the generated synthetic dataset. Further, we proposed a meta-clustering algorithm whereby the cluster centers obtained from the clients are clustered at the server for training the global model. Despite PNN being a one-pass learning classifier, its complexity depends on the training data size. Therefore, we employed a modified evolving clustering method (ECM), another one-pass algorithm to cluster the training data thereby increasing the speed further. Moreover, we conducted sensitivity analysis by varying Dthr, a hyperparameter of ECM at the server and client, one at a time. The effectiveness of our approach is validated on four finance and medical datasets.Comment: 27 pages, 13 figures, 7 table

Parallel and Streaming Wavelet Neural Networks for Classification and Regression under Apache Spark

Wavelet neural networks (WNN) have been applied in many fields to solve regression as well as classification problems. After the advent of big data, as data gets generated at a brisk pace, it is imperative to analyze it as soon as it is generated owing to the fact that the nature of the data may change dramatically in short time intervals. This is necessitated by the fact that big data is all pervasive and throws computational challenges for data scientists. Therefore, in this paper, we built an efficient Scalable, Parallelized Wavelet Neural Network (SPWNN) which employs the parallel stochastic gradient algorithm (SGD) algorithm. SPWNN is designed and developed under both static and streaming environments in the horizontal parallelization framework. SPWNN is implemented by using Morlet and Gaussian functions as activation functions. This study is conducted on big datasets like gas sensor data which has more than 4 million samples and medical research data which has more than 10,000 features, which are high dimensional in nature. The experimental analysis indicates that in the static environment, SPWNN with Morlet activation function outperformed SPWNN with Gaussian on the classification datasets. However, in the case of regression, the opposite was observed. In contrast, in the streaming environment i.e., Gaussian outperformed Morlet on the classification and Morlet outperformed Gaussian on the regression datasets. Overall, the proposed SPWNN architecture achieved a speedup of 1.32-1.40.Comment: 25 pages; 2 Tables; 7 Figure

Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection

Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC). In this study, we handled these techniques on an ATM transactions dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Further, we employed various machine learning techniques viz., Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958 AUC. DT is the winner if the complexity and interpretability aspects are considered. Among all the oversampling approaches, SMOTE and its variants were observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM secured second place with 0.947 CR. Further, we incorporated explainable artificial intelligence (XAI) and causal inference (CI) in the fraud detection framework and studied it through various analyses.Comment: 34 pages; 21 Figures; 8 Table

Parallel bi-objective evolutionary algorithms for scalable feature subset selection via migration strategy under Spark

Feature subset selection (FSS) for classification is inherently a bi-objective optimization problem, where the task is to obtain a feature subset which yields the maximum possible area under the receiver operator characteristic curve (AUC) with minimum cardinality of the feature subset. In todays world, a humungous amount of data is generated in all activities of humans. To mine such voluminous data, which is often high-dimensional, there is a need to develop parallel and scalable frameworks. In the first-of-its-kind study, we propose and develop an iterative MapReduce-based framework for bi-objective evolutionary algorithms (EAs) based wrappers under Apache spark with the migration strategy. In order to accomplish this, we parallelized the non-dominated sorting based algorithms namely non dominated sorting algorithm (NSGA-II), and non-dominated sorting particle swarm optimization (NSPSO), also the decomposition-based algorithm, namely the multi-objective evolutionary algorithm based on decomposition (MOEA-D), and named them P-NSGA-II-IS, P-NSPSO-IS, P-MOEA-D-IS, respectively. We proposed a modified MOEA-D by incorporating the non-dominated sorting principle while parallelizing it. Throughout the study, AUC is computed by logistic regression (LR). We test the effectiveness of the proposed methodology on various datasets. It is noteworthy that the P-NSGA-II turns out to be statistically significant by being in the top 2 positions on most datasets. We also reported the empirical attainment plots, speed up analysis, and mean AUC obtained by the most repeated feature subset and the least cardinal feature subset with the highest AUC, and diversity analysis using hypervolume.Comment: 32 pages, 11 Tables, 8 figure