Fase-AL -- Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning

• URL: http://arxiv.org/abs/2001.11466v1
• Date: Thu, 30 Jan 2020 17:25:47 GMT
• Title: Fase-AL -- Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning
• Authors: Agust\'in Alejandro Ortiz-D\'iaz, Fabiano Baldo, Laura Mar\'ia Palomino Mari\~no and Alberto Verdecia Cabrera
• Abstract summary: This work presents the FASE-AL algorithm which induces classification models with non-labeled instances using Active Learning. The algorithm achieves promising results in terms of the percentage of correctly classified instances.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classification algorithms to mine data stream have been extensively studied in recent years. However, a lot of these algorithms are designed for supervised learning which requires labeled instances. Nevertheless, the labeling of the data is costly and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without significant loss of model performance. Active learning is one of these paradigms, whose main objective is to build classification models that request the lowest possible number of labeled examples achieving adequate levels of accuracy. Therefore, this work presents the FASE-AL algorithm which induces classification models with non-labeled instances using Active Learning. FASE-AL is based on the algorithm Fast Adaptive Stacking of Ensembles (FASE). FASE is an ensemble algorithm that detects and adapts the model when the input data stream has concept drift. FASE-AL was compared with four different strategies of active learning found in the literature. Real and synthetic databases were used in the experiments. The algorithm achieves promising results in terms of the percentage of correctly classified instances.

Related papers

• Classification Tree-based Active Learning: A Wrapper Approach [4.706932040794696]
  This paper proposes a wrapper active learning method for classification, organizing the sampling process into a tree structure. A classification tree constructed on an initial set of labeled samples is considered to decompose the space into low-entropy regions. This adaptation proves to be a significant enhancement over existing active learning methods.
  arXiv  Detail & Related papers  (2024-04-15T17:27:00Z)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage [3.4806267677524896]
  We propose a novel active learning strategy, neural tangent kernel clustering-pseudo-labels (NTKCPL) It estimates empirical risk based on pseudo-labels and the model prediction with NTK approximation. We validate our method on five datasets, empirically demonstrating that it outperforms the baseline methods in most cases.
  arXiv  Detail & Related papers  (2023-06-07T01:43:47Z)
• Neural Active Learning on Heteroskedastic Distributions [29.01776999862397]
  We demonstrate the catastrophic failure of active learning algorithms on heteroskedastic datasets. We propose a new algorithm that incorporates a model difference scoring function for each data point to filter out the noisy examples and sample clean examples.
  arXiv  Detail & Related papers  (2022-11-02T07:30:19Z)
• Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data [1.7205106391379026]
  This paper proposes a novel Few-Shot Learning (FSL)-based AL framework, which addresses the trade-off problem by incorporating a Prototypical Network (ProtoNet) in the AL iterations. This framework was validated on UCI HAR/HAPT dataset and a real-world braking maneuver dataset. The learning performance significantly surpasses traditional AL algorithms on both datasets, achieving 90% classification accuracy with 10% and 5% labeling effort, respectively.
  arXiv  Detail & Related papers  (2022-09-28T16:14:40Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• A Lagrangian Duality Approach to Active Learning [119.36233726867992]
  We consider the batch active learning problem, where only a subset of the training data is labeled. We formulate the learning problem using constrained optimization, where each constraint bounds the performance of the model on labeled samples. We show, via numerical experiments, that our proposed approach performs similarly to or better than state-of-the-art active learning methods.
  arXiv  Detail & Related papers  (2022-02-08T19:18:49Z)
• Learning with Neighbor Consistency for Noisy Labels [69.83857578836769]
  We present a method for learning from noisy labels that leverages similarities between training examples in feature space. We evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, Clothing1M, mini-ImageNet-Red) noise.
  arXiv  Detail & Related papers  (2022-02-04T15:46:27Z)
• BALanCe: Deep Bayesian Active Learning via Equivalence Class Annealing [7.9107076476763885]
  BALanCe is a deep active learning framework that mitigates the effect of uncertainty estimates. Batch-BALanCe is a generalization of the sequential algorithm to the batched setting. We show that Batch-BALanCe achieves state-of-the-art performance on several benchmark datasets for active learning.
  arXiv  Detail & Related papers  (2021-12-27T15:38:27Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• Progressive Identification of True Labels for Partial-Label Learning [112.94467491335611]
  Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. This paper proposes a novel framework of classifier with flexibility on the model and optimization algorithm.
  arXiv  Detail & Related papers  (2020-02-19T08:35:15Z)

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.