A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification

• URL: http://arxiv.org/abs/2210.04949v2
• Date: Wed, 12 Oct 2022 06:44:50 GMT
• Title: A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
• Authors: Kleanthis Malialis and Manuel Roveri and Cesare Alippi and Christos G. Panayiotou and Marios M. Polycarpou
• Abstract summary: In real-world applications, the process generating the data might suffer from nonstationary effects. These changes, often called concept drift, might induce severe (potentially catastrophic) impacts on trained learning models. This paper aims at designing machine and deep learning models that are able to track and adapt to concept drift.
• Score: 23.69287260989898
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour). These changes, often called concept drift, might induce severe (potentially catastrophic) impacts on trained learning models that become obsolete over time, and inadequate to solve the task at hand. Learning in presence of concept drift aims at designing machine and deep learning models that are able to track and adapt to concept drift. Typically, techniques to handle concept drift are either active or passive, and traditionally, these have been considered to be mutually exclusive. Active techniques use an explicit drift detection mechanism, and re-train the learning algorithm when concept drift is detected. Passive techniques use an implicit method to deal with drift, and continually update the model using incremental learning. Differently from what present in the literature, we propose a hybrid alternative which merges the two approaches, hence, leveraging on their advantages. The proposed method called Hybrid-Adaptive REBAlancing (HAREBA) significantly outperforms strong baselines and state-of-the-art methods in terms of learning quality and speed; we experiment how it is effective under severe class imbalance levels too.

Related papers

• Normalization and effective learning rates in reinforcement learning [52.59508428613934]
  Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature. We show that normalization brings with it a subtle but important side effect: an equivalence between growth in the norm of the network parameters and decay in the effective learning rate. We propose to make the learning rate schedule explicit with a simple re- parameterization which we call Normalize-and-Project.
  arXiv  Detail & Related papers  (2024-07-01T20:58:01Z)
• Fault detection in propulsion motors in the presence of concept drift [0.0]
  Machine learning and statistical methods can be used to enhance monitoring and fault prediction in marine systems. An unexpected change in the underlying system, called a concept drift, may impact the performance of these methods. We present an approach for detecting overheating in stator windings of marine propulsion motors that is able to successfully operate during concept drift.
  arXiv  Detail & Related papers  (2024-06-12T09:31:03Z)
• Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures [37.66676003679306]
  Adapting to concept drift is a challenging task in machine learning. In communication networks, such issue emerges when performing traffic forecasting following afailure event. We propose an approach that exploits adaptive learning algorithms, namely, liquid neural networks, which are capable of self-adaptation to abrupt changes in data patterns without requiring any retraining.
  arXiv  Detail & Related papers  (2024-04-08T08:47:46Z)
• MORPH: Towards Automated Concept Drift Adaptation for Malware Detection [0.7499722271664147]
  Concept drift is a significant challenge for malware detection. Self-training has emerged as a promising approach to mitigate concept drift. We propose MORPH -- an effective pseudo-label-based concept drift adaptation method.
  arXiv  Detail & Related papers  (2024-01-23T14:25:43Z)
• Segue: Side-information Guided Generative Unlearnable Examples for Facial Privacy Protection in Real World [64.4289385463226]
  We propose Segue: Side-information guided generative unlearnable examples. To improve transferability, we introduce side information such as true labels and pseudo labels. It can resist JPEG compression, adversarial training, and some standard data augmentations.
  arXiv  Detail & Related papers  (2023-10-24T06:22:37Z)
• Unsupervised Unlearning of Concept Drift with Autoencoders [5.41354952642957]
  Concept drift refers to a change in the data distribution affecting the data stream of future samples. This paper proposes an unsupervised and model-agnostic concept drift adaptation method at the global level.
  arXiv  Detail & Related papers  (2022-11-23T14:52:49Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Autoregressive based Drift Detection Method [0.0]
  We propose a new concept drift detection method based on autoregressive models called ADDM. Our results show that this new concept drift detection method outperforms the state-of-the-art drift detection methods.
  arXiv  Detail & Related papers  (2022-03-09T14:36:16Z)
• Learning to Learn Transferable Attack [77.67399621530052]
  Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. We propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. Empirical results on the widely-used dataset demonstrate the effectiveness of our attack method with a 12.85% higher success rate of transfer attack compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-10T07:24:21Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)
• Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads [69.21503033239985]
  Transporting suspended payloads is challenging for autonomous aerial vehicles. We propose a meta-learning approach that "learns how to learn" models of altered dynamics within seconds of post-connection flight data.
  arXiv  Detail & Related papers  (2020-04-23T17:43:56Z)

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.