Drift anticipation with forgetting to improve evolving fuzzy system

• URL: http://arxiv.org/abs/2101.02442v1
• Date: Thu, 7 Jan 2021 09:21:27 GMT
• Title: Drift anticipation with forgetting to improve evolving fuzzy system
• Authors: Cl\'ement Leroy (INTUIDOC), Eric Anquetil (INTUIDOC), Nathalie Girard (INTUIDOC)
• Abstract summary: This paper proposes a coherent method to integrate forgetting in Evolving Fuzzy System. The forgetting is applied with two methods: an exponential forgetting of the premise part and a deferred directional forgetting of the conclusion part. An evaluation of the proposed methods is carried out on benchmark online datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Working with a non-stationary stream of data requires for the analysis system to evolve its model (the parameters as well as the structure) over time. In particular, concept drifts can occur, which makes it necessary to forget knowledge that has become obsolete. However, the forgetting is subjected to the stability-plasticity dilemma, that is, increasing forgetting improve reactivity of adapting to the new data while reducing the robustness of the system. Based on a set of inference rules, Evolving Fuzzy Systems-EFS-have proven to be effective in solving the data stream learning problem. However tackling the stability-plasticity dilemma is still an open question. This paper proposes a coherent method to integrate forgetting in Evolving Fuzzy System, based on the recently introduced notion of concept drift anticipation. The forgetting is applied with two methods: an exponential forgetting of the premise part and a deferred directional forgetting of the conclusion part of EFS to preserve the coherence between both parts. The originality of the approach consists in applying the forgetting only in the anticipation module and in keeping the EFS (called principal system) learned without any forgetting. Then, when a drift is detected in the stream, a selection mechanism is proposed to replace the obsolete parameters of the principal system with more suitable parameters of the anticipation module. An evaluation of the proposed methods is carried out on benchmark online datasets, with a comparison with state-of-the-art online classifiers (Learn++.NSE, PENsemble, pclass) as well as with the original system using different forgetting strategies.

Related papers

• A General Bayesian Framework for Informative Input Design in System Identification [86.05414211113627]
  We tackle the problem of informative input design for system identification. We select inputs, observe the corresponding outputs from the true system, and optimize the parameters of our model to best fit the data. Our method outperforms model-free baselines with various linear and nonlinear dynamics.
  arXiv  Detail & Related papers  (2025-01-28T01:57:51Z)
• Unsupervised learning for anticipating critical transitions [0.249660468924754]
  We articulate a framework combining a variational autoencoder (VAE) and reservoir computing to address this challenge. In particular, the driving factor is detected from time series using the VAE in an unsupervised-learning fashion. We demonstrate power of prototypical unsupervised learning scheme using dynamical systems including the Kuramoto-Sivashinsky system.
  arXiv  Detail & Related papers  (2025-01-02T23:57:23Z)
• Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection [6.3399691183255165]
  Intrusion Detection Systems (IDS) are crucial for safeguarding digital infrastructure. In this paper, we propose SSF (Strategic Selection and Forgetting), a novel continual learning method for IDS. Our approach features a strategic sample selection algorithm to select representative new samples and a strategic forgetting mechanism to drop outdated samples.
  arXiv  Detail & Related papers  (2024-12-20T09:22:07Z)
• PEARL: Input-Agnostic Prompt Enhancement with Negative Feedback Regulation for Class-Incremental Learning [17.819582979803286]
  Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones. Prompt learning has been adopted in CIL for its ability to adjust data distribution to better align with pre-trained knowledge. This paper critically examines the limitations of existing methods from the perspective of prompt learning.
  arXiv  Detail & Related papers  (2024-12-14T17:13:30Z)
• Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset [98.52916361979503]
  We introduce a novel learning approach that automatically models and adapts to non-stationarity. We show empirically that our approach performs well in non-stationary supervised and off-policy reinforcement learning settings.
  arXiv  Detail & Related papers  (2024-11-06T16:32:40Z)
• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• DIAR: Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation [10.645244994430483]
  We propose a novel offline reinforcement learning (offline RL) approach, introducing the Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation framework. We leverage diffusion models to learn state-action sequence distributions and incorporate value functions for more balanced and adaptive decision-making. As demonstrated in tasks like Maze2D, AntMaze, and Kitchen, DIAR consistently outperforms state-of-the-art algorithms in long-horizon, sparse-reward environments.
  arXiv  Detail & Related papers  (2024-10-15T07:09:56Z)
• Supervised DKRC with Images for Offline System Identification [77.34726150561087]
  Modern dynamical systems are becoming increasingly non-linear and complex. There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. Our approach learns these basis functions using a supervised learning approach.
  arXiv  Detail & Related papers  (2021-09-06T04:39:06Z)
• Learning Parameter Distributions to Detect Concept Drift in Data Streams [13.20231558027132]
  We propose a novel framework for the detection of real concept drift, called ERICS. By treating the parameters of a predictive model as random variables, we show that concept drift corresponds to a change in the distribution of optimal parameters. ERICS is also capable to detect concept drift at the input level, which is a significant advantage over existing approaches.
  arXiv  Detail & Related papers  (2020-10-19T11:19:16Z)
• Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems [91.43582419264763]
  We study the problem of system identification and adaptive control in partially observable linear dynamical systems. We present the first model estimation method with finite-time guarantees in both open and closed-loop system identification. We show that AdaptOn is the first algorithm that achieves $textpolylogleft(Tright)$ regret in adaptive control of unknown partially observable linear dynamical systems.
  arXiv  Detail & Related papers  (2020-03-25T06:00:33Z)
• Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees [49.91477656517431]
  Quantization-based solvers have been widely adopted in Federated Learning (FL) No existing methods enjoy all the aforementioned properties. We propose an intuitively-simple yet theoretically-simple method based on SIGNSGD to bridge the gap.
  arXiv  Detail & Related papers  (2020-02-25T15:12:15Z)

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.