Class-Imbalanced Learning on Graphs: A Survey

The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor performance of machine learning models. To overcome this challenge, class-imbalanced learning on graphs (CILG) has emerged as a promising solution that combines the strengths of graph representation learning and class-imbalanced learning. In recent years, significant progress has been made in CILG. Anticipating that such a trend will continue, this survey aims to offer a comprehensive understanding of the current state-of-the-art in CILG and provide insights for future research directions. Concerning the former, we introduce the first taxonomy of existing work and its connection to existing imbalanced learning literature. Concerning the latter, we critically analyze recent work in CILG and discuss urgent lines of inquiry within the topic. Moreover, we provide a continuously maintained reading list of papers and code at https://github.com/yihongma/CILG-Papers.Comment: submitted to ACM Computing Survey (CSUR

Confusion Modelling - An Estimation by Semantic Embeddings

Approaching the task of coherence assessment of a conversation from its negative perspective ‘confusion’ rather than coherence itself, has been attempted by very few research works. Influencing Embeddings to learn from similarity/dissimilarity measures such as distance, cosine similarity between two utterances will equip them with the semantics to differentiate a coherent and an incoherent conversation through the detection of negative entity, ‘confusion’. This research attempts to measure coherence of conversation between a human and a conversational agent by means of such semantic embeddings trained from scratch by an architecture centralising the learning from the distance between the embeddings. State of the art performance of general BERT’s embeddings and state of the art performance of ConveRT’s conversation specific embeddings in addition to the GLOVE embeddings are also tested upon the laid architecture. Confusion, being a more sensible entity, real human labelling performance is set as the baseline to evaluate the models. The base design resulted in not such a good performance against the human score but the pre-trained embeddings when plugged into the base architecture had performance boosts in a particular order from lowest to highest, through BERT, GLOVE and ConveRT. The intuition and the efficiency of the base conceptual design is proved of its success when the variant having the ConveRT embeddings plugged into the base design, outperformed the original ConveRT’s state of art performance on generating similarity scores. Though a performance comparable to real human performance was not achieved by the models, there witnessed a considerable overlapping between the ConveRT variant and the human scores which is really a great positive inference to be enjoyed as achieving human performance is always the state of art in any research domain. Also, from the results, this research joins the group of works claiming BERT to be unsuitable for conversation specific modelling and embedding works

A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition

This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Variance Decomposition, establishing a theoretical framework that closely relates data imbalance to model variance. We also leverage graph augmentation technique to estimate the variance, and design a regularization term to alleviate the impact of imbalance. Exhaustive tests are conducted on multiple benchmarks, including naturally imbalanced datasets and public-split class-imbalanced datasets, demonstrating that our approach outperforms state-of-the-art methods in various imbalanced scenarios. This work provides a novel theoretical perspective for addressing the problem of imbalanced node classification in GNNs.Comment: Accepted by NeurIPS 202

MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network.

Class-imbalanced datasets are common across different domains such as health, banking, security and others. With such datasets, the learning algorithms are often biased toward the majority class-instances. Data Augmentation is a common approach that aims at rebalancing a dataset by injecting more data samples of the minority class instances. In this paper, a new data augmentation approach is proposed using a Generative Adversarial Networks (GAN) to handle the class imbalance problem. Unlike common GAN models, which use a single fake class, the proposed method uses multiple fake classes to ensure a fine-grained generation and classification of the minority class instances. Moreover, the proposed GAN model is conditioned to generate minority class instances aiming at rebalancing the dataset. Extensive experiments were carried out using public datasets, where synthetic samples generated using our model were added to the imbalanced dataset, followed by performing classification using Convolutional Neural Network. Experiment results show that our model can generate diverse minority class instances, even in extreme cases where the number of minority class instances is relatively low. Additionally, superior performance of our model over other common augmentation and oversampling methods was achieved in terms of classification accuracy and quality of the generated samples

Learning from small and imbalanced dataset of images using generative adversarial neural networks.

The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and semi-supervised learning have not lived up to expectations. Some of these techniques do not account for other classification challenges, such as a class-imbalance problem. Thus, these techniques mostly underperform when compared with fully supervised approaches. In this thesis, we propose new methods to train a deep model on image classification with a limited number of labeled examples. This was achieved by extending state-of-the-art generative adversarial networks with multiple fake classes and network switchers. These new features enabled us to train a classifier using large unlabeled data, while generating class specific samples. The proposed model is label agnostic and is suitable for different classification scenarios, ranging from weakly supervised to fully supervised settings. This was used to address classification challenges with limited labeled data and a class-imbalance problem. Extensive experiments were carried out on different benchmark datasets. Firstly, the proposed approach was used to train a classification model and our findings indicated that the proposed approach achieved better classification accuracies, especially when the number of labeled samples is small. Secondly, the proposed approach was able to generate high-quality samples from class-imbalance datasets. The samples' quality is evident in improved classification performances when generated samples were used in neutralising class-imbalance. The results are thoroughly analyzed and, overall, our method showed superior performances over popular resampling technique and the AC-GAN model. Finally, we successfully applied the proposed approach as a new augmentation technique to two challenging real-world problems: face with attributes and legacy engineering drawings. The results obtained demonstrate that the proposed approach is effective even in extreme cases

MINDWALC : mining interpretable, discriminative walks for classification of nodes in a knowledge graph

Background Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity. They can directly process these type of graphs or learn a low-dimensional numerical representation. While it has been shown empirically that these techniques achieve excellent predictive performances, they lack interpretability. This is of vital importance in applications situated in critical domains, such as health care. Methods We present a technique that mines interpretable walks from knowledge graphs that are very informative for a certain classification problem. The walks themselves are of a specific format to allow for the creation of data structures that result in very efficient mining. We combine this mining algorithm with three different approaches in order to classify nodes within a graph. Each of these approaches excels on different dimensions such as explainability, predictive performance and computational runtime. Results We compare our techniques to well-known state-of-the-art black-box alternatives on four benchmark knowledge graph data sets. Results show that our three presented approaches in combination with the proposed mining algorithm are at least competitive to the black-box alternatives, even often outperforming them, while being interpretable. Conclusions The mining of walks is an interesting alternative for node classification in knowledge graphs. Opposed to the current state-of-the-art that uses deep learning techniques, it results in inherently interpretable or transparent models without a sacrifice in terms of predictive performance

신체 임베딩을 활용한 오토인코더 기반 컴퓨터 비전 모형의 성능 개선

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2021.8. 박종헌.Deep learning models have dominated the field of computer vision, achieving state-of-the-art performance in various tasks. In particular, with recent increases in images and videos of people being posted on social media, research on computer vision tasks for analyzing human visual information is being used in various ways. This thesis addresses classifying fashion styles and measuring motion similarity as two computer vision tasks related to humans. In real-world fashion style classification problems, the number of samples collected for each style class varies according to the fashion trend at the time of data collection, resulting in class imbalance. In this thesis, to cope with this class imbalance problem, generalized few-shot learning, in which both minority classes and majority classes are used for learning and evaluation, is employed. Additionally, the modalities of the foreground images, cropped to show only the body and fashion item parts, and the fashion attribute information are reflected in the fashion image embedding through a variational autoencoder. The K-fashion dataset collected from a Korean fashion shopping mall is used for the model training and evaluation. Motion similarity measurement is used as a sub-module in various tasks such as action recognition, anomaly detection, and person re-identification; however, it has attracted less attention than the other tasks because the same motion can be represented differently depending on the performer's body structure and camera angle. The lack of public datasets for model training and evaluation also makes research challenging. Therefore, we propose an artificial dataset for model training, with motion embeddings separated from the body structure and camera angle attributes for training using an autoencoder architecture. The autoencoder is designed to generate motion embeddings for each body part to measure motion similarity by body part. Furthermore, motion speed is synchronized by matching patches performing similar motions using dynamic time warping. The similarity score dataset for evaluation was collected through a crowdsourcing platform utilizing videos of NTU RGB+D 120, a dataset for action recognition. When the proposed models were verified with each evaluation dataset, both outperformed the baselines. In the fashion style classification problem, the proposed model showed the most balanced performance, without bias toward either the minority classes or the majority classes, among all the models. In addition, In the motion similarity measurement experiments, the correlation coefficient of the proposed model to the human-measured similarity score was higher than that of the baselines.컴퓨터 비전은 딥러닝 학습 방법론이 강점을 보이는 분야로, 다양한 태스크에서 우수한 성능을 보이고 있다. 특히, 사람이 포함된 이미지나 동영상을 딥러닝을 통해 분석하는 태스크의 경우, 최근 소셜 미디어에 사람이 포함된 이미지 또는 동영상 게시물이 늘어나면서 그 활용 가치가 높아지고 있다. 본 논문에서는 사람과 관련된 컴퓨터 비전 태스크 중 패션 스타일 분류 문제와 동작 유사도 측정에 대해 다룬다. 패션 스타일 분류 문제의 경우, 데이터 수집 시점의 패션 유행에 따라 스타일 클래스별 수집되는 샘플의 양이 달라지기 때문에 이로부터 클래스 불균형이 발생한다. 본 논문에서는 이러한 클래스 불균형 문제에 대처하기 위하여, 소수 샘플 클래스와 다수 샘플 클래스를 학습 및 평가에 모두 사용하는 일반화된 퓨샷러닝으로 패션 스타일 분류 문제를 설정하였다. 또한 변분 오토인코더 기반의 모델을 통해, 신체 및 패션 아이템 부분만 잘라낸 전경 이미지 모달리티와 패션 속성 정보 모달리티가 패션 이미지의 임베딩 학습에 반영되도록 하였다. 학습 및 평가를 위한 데이터셋으로는 한국 패션 쇼핑몰에서 수집된 K-fashion 데이터셋을 사용하였다. 한편, 동작 유사도 측정은 행위 인식, 이상 동작 감지, 사람 재인식 같은 다양한 분야의 하위 모듈로 활용되고 있지만 그 자체가 연구된 적은 많지 않은데, 이는 같은 동작을 수행하더라도 신체 구조 및 카메라 각도에 따라 다르게 표현될 수 있다는 점으로 부터 기인한다. 학습 및 평가를 위한 공개 데이터셋이 많지 않다는 점 또한 연구를 어렵게 하는 요인이다. 따라서 본 논문에서는 학습을 위한 인공 데이터셋을 수집하여 오토인코더 구조를 통해 신체 구조 및 카메라 각도 요소가 분리된 동작 임베딩을 학습하였다. 이때, 각 신체 부위별로 동작 임베딩을 생성할 수 있도록하여 신체 부위별로 동작 유사도 측정이 가능하도록 하였다. 두 동작 사이의 유사도를 측정할 때에는 동적 시간 워핑 기법을 사용, 비슷한 동작을 수행하는 구간끼리 정렬시켜 유사도를 측정하도록 함으로써, 동작 수행 속도의 차이를 보정하였다. 평가를 위한 유사도 점수 데이터셋은 행위 인식 데이터셋인 NTU-RGB+D 120의 영상을 활용하여 크라우드 소싱 플랫폼을 통해 수집되었다. 두 가지 태스크의 제안 모델을 각각의 평가 데이터셋으로 검증한 결과, 모두 비교 모델 대비 우수한 성능을 기록하였다. 패션 스타일 분류 문제의 경우, 모든 비교군에서 소수 샘플 클래스와 다수 샘플 클래스 중 한 쪽으로 치우치지 않는 가장 균형잡힌 추론 성능을 보여주었고, 동작 유사도 측정의 경우 사람이 측정한 유사도 점수와 상관계수에서 비교 모델 대비 더 높은 수치를 나타내었다.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Research contribution 5 1.2.1 Fashion style classication 5 1.2.2 Human motion similarity 9 1.2.3 Summary of the contributions 11 1.3 Thesis outline 13 Chapter 2 Literature Review 14 2.1 Fashion style classication 14 2.1.1 Machine learning and deep learning-based approaches 14 2.1.2 Class imbalance 15 2.1.3 Variational autoencoder 17 2.2 Human motion similarity 19 2.2.1 Measuring the similarity between two people 19 2.2.2 Human body embedding 20 2.2.3 Datasets for measuring the similarity 20 2.2.4 Triplet and quadruplet losses 21 2.2.5 Dynamic time warping 22 Chapter 3 Fashion Style Classication 24 3.1 Dataset for fashion style classication: K-fashion 24 3.2 Multimodal variational inference for fashion style classication 28 3.2.1 CADA-VAE 31 3.2.2 Generating multimodal features 33 3.2.3 Classier training with cyclic oversampling 36 3.3 Experimental results for fashion style classication 38 3.3.1 Implementation details 38 3.3.2 Settings for experiments 42 3.3.3 Experimental results on K-fashion 44 3.3.4 Qualitative analysis 48 3.3.5 Eectiveness of the cyclic oversampling 50 Chapter 4 Motion Similarity Measurement 53 4.1 Datasets for motion similarity 53 4.1.1 Synthetic motion dataset: SARA dataset 53 4.1.2 NTU RGB+D 120 similarity annotations 55 4.2 Framework for measuring motion similarity 58 4.2.1 Body part embedding model 58 4.2.2 Measuring motion similarity 67 4.3 Experimental results for measuring motion similarity 68 4.3.1 Implementation details 68 4.3.2 Experimental results on NTU RGB+D 120 similarity annotations 72 4.3.3 Visualization of motion latent clusters 78 4.4 Application 81 4.4.1 Real-world application with dancing videos 81 4.4.2 Tuning similarity scores to match human perception 87 Chapter 5 Conclusions 89 5.1 Summary and contributions 89 5.2 Limitations and future research 91 Appendices 93 Chapter A NTU RGB+D 120 Similarity Annotations 94 A.1 Data collection 94 A.2 AMT score analysis 95 Chapter B Data Cleansing of NTU RGB+D 120 Skeletal Data 100 Chapter C Motion Sequence Generation Using Mixamo 102 Bibliography 104 국문초록 123박