A Survey on the New Generation of Deep Learning in Image Processing <s> 2) CLASS IMBALANCE FOR DEEP LEARNING <s> The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 2) CLASS IMBALANCE FOR DEEP LEARNING <s> In lots of natural language processing tasks, the classes to be dealt with often occur heavily imbalanced in the underlying data set and classifiers trained on such skewed data tend to exhibit poor performance for low-frequency classes. We introduce and compare different approaches to reduce class imbalance by design within the context of active learning (AL). Our goal is to compile more balanced data sets up front during annotation time when AL is used as a strategy to acquire training material. We situate our approach in the context of named entity recognition. Our experiments reveal that we can indeed reduce class imbalance and increase the performance of classifiers on minority classes while preserving a good overall performance in terms of macro F-score. <s> BIB002 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 2) CLASS IMBALANCE FOR DEEP LEARNING <s> The class imbalance problem is encountered in real-world applications of machine learning and results in a classifier's suboptimal performance. Researchers have rigorously studied the resampling, algorithms, and feature selection approaches to this problem. No systematic studies have been conducted to understand how well these methods combat the class imbalance problem and which of these methods best manage the different challenges posed by imbalanced data sets. In particular, feature selection has rarely been studied outside of text classification problems. Additionally, no studies have looked at the additional problem of learning from small samples. This paper presents a first systematic comparison of the three types of methods developed for imbalanced data classification problems and of seven feature selection metrics evaluated on small sample data sets from different applications. We evaluated the performance of these metrics using area under the receiver operating characteristic (AUC) and area under the precision-recall curve (PRC). We compared each metric on the average performance across all problems and on the likelihood of a metric yielding the best performance on a specific problem. We examined the performance of these metrics inside each problem domain. Finally, we evaluated the efficacy of these metrics to see which perform best across algorithms. Our results showed that signal-to-noise correlation coefficient (S2N) and Feature Assessment by Sliding Thresholds (FAST) are great candidates for feature selection in most applications, especially when selecting very small numbers of features. <s> BIB003 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 2) CLASS IMBALANCE FOR DEEP LEARNING <s> In this paper, we propose an extended deep learning approach that incorporates instance selection and bootstrapping techniques for imbalanced data classification. In supervised learning, classification performance often deteriorates when the training set is imbalanced where at least one of the classes has a substantially fewer number of instances than the others. We propose to use adaptive synthetic sampling approach (ADASYN) to generate synthetic instances for the minority class. A data pruning process based on multiple correspondence analysis (MCA) is then performed to identify a sub-set of synthetic instances that are most suitable to supplement the existing minority instances. This results in a relatively more balanced training dataset which is then bootstrapped and fed into the convolutional neural networks (CNNs) for classification. Furthermore, we propose to use low-level features pre-processed by principal component analysis (PCA), instead of the commonly used raw signal data, as the input to CNNs to reduce the computational time. The experimental results show the effectiveness of our framework in classifying 54 TRECVID concepts with different imbalanced levels by comparing with other state-of-the-art methods. <s> BIB004 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 2) CLASS IMBALANCE FOR DEEP LEARNING <s> Nothing is as important as human lives. There are so many cases where we need to predict the things, the causes that lead to the destruction of mankind. Such things occur occasionally but can be destructive. Therefore their prediction is very important so as to solve them at the early and safe stage. Such data is said to be imbalanced where negative cases outnumber the positive cases by huge proportions and the prediction of these rare occurring positive cases is very important. So far all the machine learners are biased towards the majority class. In this overview we are exploring all the techniques that have been used to mine the imbalanced data sets. Techniques at pre-processing level, algorithmic level are being discussed in this review. Also ensemble and hybrid techniques are being reviewed. In this paper, techniques of two types of imbalanced data sets are being reviewed viz binary class imbalanced data and multi class imbalanced data. <s> BIB005 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 2) CLASS IMBALANCE FOR DEEP LEARNING <s> The classification in class imbalanced data has drawn significant interest in medical application. Most existing methods are prone to categorize the samples into the majority class, resulting in bias, in particular the insufficient identification of minority class. A kind of novel approach, class weights random forest is introduced to address the problem, by assigning individual weights for each class instead of a single weight. The validation test on UCI data sets demonstrates that for imbalanced medical data, the proposed method enhanced the overall performance of the classifier while producing high accuracy in identifying both majority and minority class. <s> BIB006 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 2) CLASS IMBALANCE FOR DEEP LEARNING <s> Most deep learning classification studies assume clean data. However, when dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance, and 3) missing label problems. These problems undermine the performance of a classifier. Various preprocessing techniques have been proposed to mitigate one of these problems, but an algorithm that assumes and resolves all three problems together has not been proposed yet. In this paper, we propose HexaGAN, a generative adversarial network framework that shows promising classification performance for all three problems. We interpret the three problems from a single perspective to solve them jointly. To enable this, the framework consists of six components, which interact with each other. We also devise novel loss functions corresponding to the architecture. The designed loss functions allow us to achieve state-of-the-art imputation performance, with up to a 14% improvement, and to generate high-quality class-conditional data. We evaluate the classification performance (F1-score) of the proposed method with 20% missingness and confirm up to a 5% improvement in comparison with the performance of combinations of state-of-the-art methods. <s> BIB007
Classification of imbalanced data sets is a significant research problem as many real-world image sets have skewed class distributions in which most images belong to a few majority classes, and the minority classes contain a limited amount of other image sets  . Many approaches have been proposed for tackling this issue, and these approaches can be roughly divided into two categories, • The first is data manipulation techniques that target changing the data distribution to make data sets less imbalanced. For example, the most common approach is the sampling, which can operate on the data itself to increase its balance. However, over-sampling can quickly introduce undesirable noise with over-fitting risks, and under-sampling is often preferred to remove valuable information  . • The second is algorithm/model-oriented approaches, which aim to develop new learning mechanisms to work for imbalanced images datasets. For example, costsensitive learning, which can assign higher misclassification costs to the minority class than to the majority. More common class imbalanced methods in machine learning can refer to literature BIB003 , BIB005 . Although deep learning has reached great success in many research topics, as mentioned earlier, very few approaches have been made to target it for imbalanced image data. Undoubtedly, applying deep learning directly on imbalanced images datasets may result in poor performance. Below, we introduce four approaches that may help improve the performance of deep learning in class imbalance. • The first is a deep generative model, which can generate more new images with some variations from the minority classes. For example, HexaGAN, a generative adversarial network framework that shows promising classification performance for class imbalance problem BIB007 . However, the convergence of deep generative models based on minority class image datasets becomes an insurmountable challenge. • The second is the deep forest, which can give full play to the superiority of its basic unit decision tree in class imbalance. For example, class weights random forest can be assigned individual weights for each class instead of a single weight BIB006 . The validation test on UCI data sets demonstrates that for imbalanced medical data, class weights random forest enhanced the overall performance of the classifier while producing high accuracy in identifying both majority and minority class. • The third is to combine deep CNN with bootstrapping strategy, and during the bootstrapping process, a set of pseudo balanced training batches are generated based on the properties of the data set and fed into the deep CNN for classification BIB004 . • The fourth is active learning, which is a more efficient alternative to resampling methods to form a balanced training image datasets. Naturally, a model that combines deep learning with active learning can be one of the most promising direction for class imbalance BIB001 , BIB002 .