scholarly journals Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering

2014 ◽  
pp. 61-68 ◽  
Author(s):  
José A. Sáez ◽  
Julián Luengo ◽  
Jerzy Stefanowski ◽  
Francisco Herrera
Keyword(s):  
Imbalanced Classification ◽  
Ensemble Filtering ◽  
Noisy Examples

A hybrid CP/MOLS approach for multi-objective imbalanced classification

2021 ◽  
Author(s):  
Nicolas Szczepanski ◽  
Gilles Audemard ◽  
Laetitia Jourdan ◽  
Christophe Lecoutre ◽  
Lucien Mousin ◽  
...  
Keyword(s):  
Imbalanced Classification ◽  
Multi Objective

scholarly journals Laplacian networks: bounding indicator function smoothness for neural networks robustness

2021 ◽  
Vol 10 ◽  
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Supervised Learning ◽  
Indicator Function ◽  
Training Data ◽  
Theoretical Justification ◽  
The Past ◽  
Noisy Examples

For the past few years, deep learning (DL) robustness (i.e. the ability to maintain the same decision when inputs are subject to perturbations) has become a question of paramount importance, in particular in settings where misclassification can have dramatic consequences. To address this question, authors have proposed different approaches, such as adding regularizers or training using noisy examples. In this paper we introduce a regularizer based on the Laplacian of similarity graphs obtained from the representation of training data at each layer of the DL architecture. This regularizer penalizes large changes (across consecutive layers in the architecture) in the distance between examples of different classes, and as such enforces smooth variations of the class boundaries. We provide theoretical justification for this regularizer and demonstrate its effectiveness to improve robustness on classical supervised learning vision datasets for various types of perturbations. We also show it can be combined with existing methods to increase overall robustness.

The effect of rebalancing on LDA in imbalanced classification †

Stat ◽  
2021 ◽  
Author(s):  
Arlene K. H. Kim ◽  
Hyunwoo Chung
Keyword(s):  
Imbalanced Classification

Score matching filters

2021 ◽  
Author(s):  
Marie Turčičová ◽  
Jan Mandel ◽  
Kryštof Eben
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Markov Random Fields ◽  
Computational Cost ◽  
Time Step ◽  
Sample Covariance ◽  
Monte Carlo Approximation ◽  
Gauss Markov Random Fields ◽  
Ensemble Filtering ◽  
Non Gaussian

<p>A widely popular group of data assimilation methods in meteorological and geophysical sciences is formed by filters based on Monte-Carlo approximation of the traditional Kalman filter, e.g. <span>E</span><span>nsemble Kalman filter </span><span>(EnKF)</span><span>, </span><span>E</span><span>nsemble </span><span>s</span><span>quare-root filter and others. Due to the computational cost, ensemble </span><span>size </span><span>is </span><span>usually </span><span>small </span><span>compar</span><span>ed</span><span> to the dimension of the </span><span>s</span><span>tate </span><span>vector. </span><span>Traditional </span> <span>EnKF implicitly uses the sample covariance which is</span><span> a poor estimate of the </span><span>background covariance matrix - singular and </span><span>contaminated by </span><span>spurious correlations. </span></p><p><span>W</span><span>e focus on modelling the </span><span>background </span><span>covariance matrix by means of </span><span>a linear model for its inverse. This is </span><span>particularly </span><span>useful</span> <span>in</span><span> Gauss-Markov random fields (GMRF), </span><span>where</span> <span>the inverse covariance matrix has </span><span>a banded </span><span>structure</span><span>. </span><span>The parameters of the model are estimated by the</span><span> score matching </span><span>method which </span><span>provides</span><span> estimators in a closed form</span><span>, cheap to compute</span><span>. The resulting estimate</span><span> is a key component of the </span><span>proposed </span><span>ensemble filtering algorithms. </span><span>Under the assumption that the state vector is a GMRF in every time-step, t</span><span>he Score matching filter with Gaussian resamplin</span><span>g (SMF-GR) </span><span>gives</span><span> in every time-step a consistent (in the large ensemble limit) estimator of mean and covariance matrix </span><span>of the forecast and analysis distribution</span><span>. Further, we propose a filtering method called Score matching ensemble filter (SMEF), based on regularization of the EnK</span><span>F</span><span>. Th</span><span>is</span><span> filter performs well even for non-Gaussian systems with non-linear dynamic</span><span>s</span><span>. </span><span>The performance of both filters is illustrated on a simple linear convection model and Lorenz-96.</span></p>

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