Adaptive Learning for the Resource-Constrained Classification Problem

• URL: http://arxiv.org/abs/2207.09196v1
• Date: Tue, 19 Jul 2022 11:00:33 GMT
• Title: Adaptive Learning for the Resource-Constrained Classification Problem
• Authors: Danit Shifman Abukasis, Izack Cohen, Xiaochen Xian, Kejun Huang, Gonen Singer
• Abstract summary: Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis. We design an adaptive learning approach that considers resource constraints and learning jointly by iteratively fine-tuning misclassification costs. We envision the adaptive learning approach as an important addition to the repertoire of techniques for handling resource-constrained classification problems.
• Score: 14.19197444541245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufacturing settings under a limited inspection budget. Typical classification algorithms treat the learning process and the resource constraints as two separate and sequential tasks. Here we design an adaptive learning approach that considers resource constraints and learning jointly by iteratively fine-tuning misclassification costs. Via a structured experimental study using a publicly available data set, we evaluate a decision tree classifier that utilizes the proposed approach. The adaptive learning approach performs significantly better than alternative approaches, especially for difficult classification problems in which the performance of common approaches may be unsatisfactory. We envision the adaptive learning approach as an important addition to the repertoire of techniques for handling resource-constrained classification problems.

Related papers

• Optimal Baseline Corrections for Off-Policy Contextual Bandits [61.740094604552475]
  We aim to learn decision policies that optimize an unbiased offline estimate of an online reward metric. We propose a single framework built on their equivalence in learning scenarios. Our framework enables us to characterize the variance-optimal unbiased estimator and provide a closed-form solution for it.
  arXiv  Detail & Related papers  (2024-05-09T12:52:22Z)
• Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation [73.83178465971552]
  The success of automated medical image analysis depends on large-scale and expert-annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection. We propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective.
  arXiv  Detail & Related papers  (2023-07-27T08:58:05Z)
• Resilient Constrained Learning [94.27081585149836]
  This paper presents a constrained learning approach that adapts the requirements while simultaneously solving the learning task. We call this approach resilient constrained learning after the term used to describe ecological systems that adapt to disruptions by modifying their operation.
  arXiv  Detail & Related papers  (2023-06-04T18:14:18Z)
• Continual Learning For On-Device Environmental Sound Classification [63.81276321857279]
  We propose a simple and efficient continual learning method for on-device environmental sound classification. Our method selects the historical data for the training by measuring the per-sample classification uncertainty.
  arXiv  Detail & Related papers  (2022-07-15T12:13:04Z)
• The Statistical Complexity of Interactive Decision Making [126.04974881555094]
  We provide a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning. A unified algorithm design principle, Estimation-to-Decisions (E2D), transforms any algorithm for supervised estimation into an online algorithm for decision making.
  arXiv  Detail & Related papers  (2021-12-27T02:53:44Z)
• Active Weighted Aging Ensemble for Drifted Data Stream Classification [2.277447144331876]
  Concept drift destabilizes the performance of the classification model and seriously degrades its quality. The proposed method has been evaluated through computer experiments using both real and generated data streams. The results confirm the high quality of the proposed algorithm over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-19T13:52:53Z)
• Adaptive Discretization in Online Reinforcement Learning [9.560980936110234]
  Two major questions in designing discretization-based algorithms are how to create the discretization and when to refine it. We provide a unified theoretical analysis of tree-based hierarchical partitioning methods for online reinforcement learning. Our algorithms are easily adapted to operating constraints, and our theory provides explicit bounds across each of the three facets.
  arXiv  Detail & Related papers  (2021-10-29T15:06:15Z)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)
• Learning to Actively Learn: A Robust Approach [22.75298609290053]
  This work proposes a procedure for designing algorithms for adaptive data collection tasks like active learning and pure-exploration multi-armed bandits. Our adaptive algorithm is learned via adversarial training over equivalence classes of problems derived from information theoretic lower bounds. We perform synthetic experiments to justify the stability and effectiveness of the training procedure, and then evaluate the method on tasks derived from real data.
  arXiv  Detail & Related papers  (2020-10-29T06:48:22Z)
• Sparse Methods for Automatic Relevance Determination [0.0]
  We first review automatic relevance determination (ARD) and analytically demonstrate the need to additional regularization or thresholding to achieve sparse models. We then discuss two classes of methods, regularization based and thresholding based, which build on ARD to learn parsimonious solutions to linear problems.
  arXiv  Detail & Related papers  (2020-05-18T14:08:49Z)
• Probabilistic Diagnostic Tests for Degradation Problems in Supervised Learning [0.0]
  Problems such as class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness limit accuracy in classification algorithms. Probability diagnostic model based on identifying signs and symptoms of each problem is presented. Behavior and performance of several supervised algorithms are studied when training sets have such problems.
  arXiv  Detail & Related papers  (2020-04-06T20:32:35Z)

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.