Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care

• URL: http://arxiv.org/abs/2305.09907v2
• Date: Fri, 04 Apr 2025 09:52:35 GMT
• Title: Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care
• Authors: Vivek Yelleti, Ch Priyanka,
• Abstract summary: In the era of real-time data, traditional methods often struggle to keep pace with the dynamic nature of streaming environments.<n>In this paper, we proposed a hybrid framework where the model is built once and evaluated in a real-time environment.<n>We employed 8 distinct state-of-the-art outlier detection models, including one-class support vector machine (OCSVM), isolation forest adaptive sliding window approach (IForest ASD), exact storm (ES), angle-based outlier detection (ABOD), local outlier factor (LOF), Kitsunes online algorithm (KitNet), and K-nearest neighbour
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the era of real-time data, traditional methods often struggle to keep pace with the dynamic nature of streaming environments. In this paper, we proposed a hybrid framework where in (i) stage-I follows a traditional approach where the model is built once and evaluated in a real-time environment, and (ii) stage-II employs an incremental learning approach where the model is continuously retrained as new data arrives, enabling it to adapt and stay up to date. To implement these frameworks, we employed 8 distinct state-of-the-art outlier detection models, including one-class support vector machine (OCSVM), isolation forest adaptive sliding window approach (IForest ASD), exact storm (ES), angle-based outlier detection (ABOD), local outlier factor (LOF), Kitsunes online algorithm (KitNet), and K-nearest neighbour conformal density and distance based (KNN CAD). We evaluated the performance of these models across seven financial and healthcare prediction tasks, including credit card fraud detection, churn prediction, Ethereum fraud detection, heart stroke prediction, and diabetes prediction. The results indicate that our proposed incremental learning framework significantly improves performance, particularly on highly imbalanced datasets. Among all models, the IForest ASD model consistently ranked among the top three best-performing models, demonstrating superior effectiveness across various datasets.

Related papers

• Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting [0.8568432695376288]
  This paper presents an advanced Federated Learning (FL) framework for forecasting complextemporal data, improving upon recent state-of-the-art models. The resulting architecture significantly improves the model's capacity to handle complex temporal patterns in diverse forecasting applications. The efficiency of our approach is demonstrated through extensive experiments on real-world applications, including public datasets for multimodal transport demand forecasting and private datasets for Origin-Destination (OD) matrix forecasting in urban areas.
  arXiv  Detail & Related papers  (2025-03-06T15:16:57Z)
• More precise edge detections [0.0]
  Edge detection (ED) is a base task in computer vision. Current models still suffer from unsatisfactory precision rates. Model architecture for more precise predictions still needs an investigation.
  arXiv  Detail & Related papers  (2024-07-29T13:24:55Z)
• Deep Learning-Based Cyber-Attack Detection Model for Smart Grids [6.642400003243118]
  A novel artificial intelligence-based cyber-attack detection model is developed to stop data integrity cyber-attacks (DIAs) on the received load data by supervisory control and data acquisition (SCADA) In the proposed model, first the load data is forecasted using a regression model and after processing stage, the processed data is clustered using the unsupervised learning method. The proposed EE-BiLSTM method can perform more robust and accurate compared to the other two methods.
  arXiv  Detail & Related papers  (2023-12-14T10:54:04Z)
• Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching [77.133400999703]
  Correlation based stereo matching has achieved outstanding performance. Current methods with a fixed model do not work uniformly well across various datasets. This paper proposes a new perspective to dynamically calculate correlation for robust stereo matching.
  arXiv  Detail & Related papers  (2023-07-26T09:47:37Z)
• Maintaining Stability and Plasticity for Predictive Churn Reduction [8.971668467496055]
  We propose a solution called Accumulated Model Combination (AMC) AMC is a general technique and we propose several instances of it, each having their own advantages depending on the model and data properties.
  arXiv  Detail & Related papers  (2023-05-06T20:56:20Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Provable Robustness for Streaming Models with a Sliding Window [51.85182389861261]
  In deep learning applications such as online content recommendation and stock market analysis, models use historical data to make predictions. We derive robustness certificates for models that use a fixed-size sliding window over the input stream. Our guarantees hold for the average model performance across the entire stream and are independent of stream size, making them suitable for large data streams.
  arXiv  Detail & Related papers  (2023-03-28T21:02:35Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation [62.458968086881555]
  Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains. We propose a Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART) to address the challenge in CVDA.
  arXiv  Detail & Related papers  (2023-03-18T16:40:10Z)
• A prediction and behavioural analysis of machine learning methods for modelling travel mode choice [0.26249027950824505]
  We conduct a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice. Results indicate that the models with the highest disaggregate predictive performance provide poorer estimates of behavioural indicators and aggregate mode shares. It is also observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness to Pay.
  arXiv  Detail & Related papers  (2023-01-11T11:10:32Z)
• Compound Density Networks for Risk Prediction using Electronic Health Records [1.1786249372283562]
  We propose an integrated end-to-end approach by utilizing a Compound Density Network (CDNet) CDNet allows the imputation method and prediction model to be tuned together within a single framework. We validate CDNet on the mortality prediction task on the MIMIC-III dataset.
  arXiv  Detail & Related papers  (2022-08-02T09:04:20Z)
• CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships [8.679073301435265]
  We construct a new benchmark for evaluating and improving model robustness by applying perturbations to existing data. We use these labels to perturb the data by deleting non-causal agents from the scene. Under non-causal perturbations, we observe a $25$-$38%$ relative change in minADE as compared to the original.
  arXiv  Detail & Related papers  (2022-07-07T21:28:23Z)
• Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection [40.21502451136054]
  This work presents DGHL, a new family of generative models for time series anomaly detection. A top-down Convolution Network maps a novel hierarchical latent space to time series windows, exploiting temporal dynamics to encode information efficiently. Our method outperformed current state-of-the-art models on four popular benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-15T17:19:44Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• MEMO: Test Time Robustness via Adaptation and Augmentation [131.28104376280197]
  We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable.
  arXiv  Detail & Related papers  (2021-10-18T17:55:11Z)
• Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models [4.353029347463806]
  We propose an adversarial attack framework by treating the prediction model as a black-box. The adversary can oracle the prediction model with any input and obtain corresponding output. To test the attack effectiveness, two state of the art, graph neural network-based models (GCGRNN and DCRNN) are examined.
  arXiv  Detail & Related papers  (2021-10-17T03:45:35Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.