Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data

• URL: http://arxiv.org/abs/2004.02273v1
• Date: Sun, 5 Apr 2020 18:29:36 GMT
• Title: Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
• Authors: Riccardo La Grassa, Ignazio Gallo, Nicola Landro
• Abstract summary: A typical issue in Pattern Recognition is the non-uniformly sampled data. In this paper, we propose a one-class classifier based on the minimum spanning tree with a dynamic decision boundary.
• Score: 0.9569316316728905
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions. Generally, the data is considered non-uniformly sampled when in a specific area of data space, they are not enough, leading us to misclassification problems. This issue cut down the goal of the one-class classifiers decreasing their performance. In this paper, we propose a one-class classifier based on the minimum spanning tree with a dynamic decision boundary (OCdmst) to make good prediction also in the case we have non-uniformly sampled data. To prove the effectiveness and robustness of our approach we compare with the most recent one-class classifier reaching the state-of-the-art in most of them.

Related papers

• Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker [57.49330031751386]
  We find achievable information-theoretic lower bounds on loss in the presence of a test-time attacker for multi-class classifiers on any discrete dataset. We provide a general framework for finding the optimal 0-1 loss that revolves around the construction of a conflict hypergraph from the data and adversarial constraints.
  arXiv  Detail & Related papers  (2023-02-21T15:17:13Z)
• Generative Robust Classification [3.4773470589069477]
  Training adversarially robust discriminative (i.e., softmax) classification has been the dominant approach to robust classification. We investigate using adversarial training (AT)-based generative models. We find it straightforward to apply advanced data augmentation to achieve better robustness in our approach.
  arXiv  Detail & Related papers  (2022-12-14T15:33:11Z)
• Determination of class-specific variables in nonparametric multiple-class classification [0.0]
  We propose a probability-based nonparametric multiple-class classification method, and integrate it with the ability of identifying high impact variables for individual class. We report the properties of the proposed method, and use both synthesized and real data sets to illustrate its properties under different classification situations.
  arXiv  Detail & Related papers  (2022-05-07T10:08:58Z)
• Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties [62.997667081978825]
  In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Oversampling and undersampling techniques are well-known strategies to deal with this problem by balancing the number of examples of each class.
  arXiv  Detail & Related papers  (2021-12-15T18:56:39Z)
• Does Adversarial Oversampling Help us? [10.210871872870737]
  We propose a three-player adversarial game-based end-to-end method to handle class imbalance in datasets. Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach. The effectiveness of our proposed method has been validated with high-dimensional, highly imbalanced and large-scale multi-class datasets.
  arXiv  Detail & Related papers  (2021-08-20T05:43:17Z)
• Self-Trained One-class Classification for Unsupervised Anomaly Detection [56.35424872736276]
  Anomaly detection (AD) has various applications across domains, from manufacturing to healthcare. In this work, we focus on unsupervised AD problems whose entire training data are unlabeled and may contain both normal and anomalous samples. To tackle this problem, we build a robust one-class classification framework via data refinement. We show that our method outperforms state-of-the-art one-class classification method by 6.3 AUC and 12.5 average precision.
  arXiv  Detail & Related papers  (2021-06-11T01:36:08Z)
• Minimax Active Learning [61.729667575374606]
  Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either rely on model uncertainty to select the most uncertain samples or use clustering or reconstruction to choose the most diverse set of unlabeled examples. We develop a semi-supervised minimax entropy-based active learning algorithm that leverages both uncertainty and diversity in an adversarial manner.
  arXiv  Detail & Related papers  (2020-12-18T19:03:40Z)
• Predicting Classification Accuracy When Adding New Unobserved Classes [8.325327265120283]
  We study how a classifier's performance can be used to extrapolate its expected accuracy on a larger, unobserved set of classes. We formulate a robust neural-network-based algorithm, "CleaneX", which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes.
  arXiv  Detail & Related papers  (2020-10-28T14:37:25Z)
• Identifying Wrongly Predicted Samples: A Method for Active Learning [6.976600214375139]
  We propose a simple sample selection criterion that moves beyond uncertainty. We show state-of-the-art results and better rates at identifying wrongly predicted samples.
  arXiv  Detail & Related papers  (2020-10-14T09:00:42Z)
• Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier [68.38233199030908]
  Long-tail recognition tackles the natural non-uniformly distributed data in realworld scenarios. While moderns perform well on populated classes, its performance degrades significantly on tail classes. Deep-RTC is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions.
  arXiv  Detail & Related papers  (2020-07-20T05:57:42Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)

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.