skip to main content


Title: Discriminative Sample Generation for Deep Imbalanced Learning
In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions. DVAE is designed to alleviate the class imbalance by explicitly learning class boundaries between training samples, and uses learned class boundaries to guide the feature learning and sample generation. To learn class boundaries, DVAE learns a latent two-component mixture distributor, conditioned by the class labels, so the latent features can help differentiate minority class vs. majority class samples. In order to balance the training data for deep learning to emphasize on the minority class, we combine DVAE and generative adversarial networks (GAN) to form a unified model, DVAAN, which generates synthetic instances close to the class boundaries as training data to learn latent features and update the model. Experiments and comparisons confirm that DVAAN significantly alleviates the class imbalance and delivers accurate models for deep learning from imbalanced data.  more » « less
Award ID(s):
1828181 1763452
NSF-PAR ID:
10199468
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), August 10-16 2019, Macao, China
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. In many real-world classification applications such as fake news detection, the training data can be extremely imbalanced, which brings challenges to existing classifiers as the majority classes dominate the loss functions of classifiers. Oversampling techniques such as SMOTE are effective approaches to tackle the class imbalance problem by producing more synthetic minority samples. Despite their success, the majority of existing oversampling methods only consider local data distributions when generating minority samples, which can result in noisy minority samples that do not fit global data distributions or interleave with majority classes. Hence, in this paper, we study the class imbalance problem by simultaneously exploring local and global data information since: (i) the local data distribution could give detailed information for generating minority samples; and (ii) the global data distribution could provide guidance to avoid generating outliers or samples that interleave with majority classes. Specifically, we propose a novel framework GL-GAN, which leverages the SMOTE method to explore local distribution in a learned latent space and employs GAN to capture the global information, so that synthetic minority samples can be generated under even extremely imbalanced scenarios. Experimental results on diverse real data sets demonstrate the effectiveness of our GL-GAN framework in producing realistic and discriminative minority samples for improving the classification performance of various classifiers on imbalanced training data. Our code is available at https://github.com/wentao-repo/GL-GAN. 
    more » « less
  2. Producing high-quality labeled data is a challenge in any supervised learning problem, where in many cases, human involvement is necessary to ensure the label quality. However, human annotations are not flawless, especially in the case of a challenging problem. In nontrivial problems, the high disagreement among annotators results in noisy labels, which affect the performance of any machine learning model. In this work, we consider three noise reduction strategies to improve the label quality in the Article-Comment Alignment Problem, where the main task is to classify article-comment pairs according to their relevancy level. The first considered labeling disagreement reduction strategy utilizes annotators' background knowledge during the label aggregation step. The second strategy utilizes user disagreement during the training process. In the third and final strategy, we ask annotators to perform corrections and relabel the examples with noisy labels. We deploy these strategies and compare them to a resampling strategy for addressing the class imbalance, another common supervised learning challenge. These alternatives were evaluated on ACAP, a multiclass text pairs classification problem with highly imbalanced data, where one of the classes represents at most 15% of the dataset's entire population. Our results provide evidence that considered strategies can reduce disagreement between annotators. However, data quality improvement is insufficient to enhance classification accuracy in the article-comment alignment problem, which exhibits a high-class imbalance. The model performance is enhanced for the same problem by addressing the imbalance issue with a weight loss-based class distribution resampling. We show that allowing the model to pay more attention to the minority class during the training process with the presence of noisy examples improves the test accuracy by 3%. 
    more » « less
  3. null (Ed.)

    Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization.

     
    more » « less
  4. null (Ed.)
    Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DRGCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization. 
    more » « less
  5. Abstract Motivation

    Reconstructing the full-length expressed transcripts (a.k.a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak.

    Results

    We present DeepBound, an effective approach to identify boundaries of expressed transcripts from RNA-seq reads alignment. In its core DeepBound employs deep convolutional neural fields to learn the hidden distributions and patterns of boundaries. To accurately model the transition probabilities and to solve the label-imbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objective function. To address the issue that deep probabilistic graphical models requires large number of labeled training samples, we propose to use simulated RNA-seq datasets to train our model. Through extensive experimental studies on both simulation datasets of two species and biological datasets, we show that DeepBound consistently and significantly outperforms the two existing methods.

    Availability and implementation

    DeepBound is freely available at https://github.com/realbigws/DeepBound.

     
    more » « less