Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Data imbalance <s> Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN. <s> BIB001 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Data imbalance <s> In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery. <s> BIB002 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Data imbalance <s> Imbalance data are defined as a dataset whose proportion of classes is severely skewed. Classification performance of existing models tends to deteriorate due to class distribution imbalance. In addition, over-representation by majority classes prevents a classifier from paying attention to minority classes, which are generally more interesting. An effective ensemble classification method called RHSBoost has been proposed to address the imbalance classification problem. This classification rule uses random undersampling and ROSE sampling under a boosting scheme. According to the experimental results, RHSBoost appears to be an attractive classification model for imbalance data. <s> BIB003 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Data imbalance <s> For solving multi-label classification problems, traditional random K-labelsets method has two main drawbacks: 1) the randomly selected label set may result in highly imbalanced data for single-label multi-class learning, and 2) the dependency relations among different labels in the same label set may cause serious information redundancy and overlap. Both of these two drawbacks can affect the generalization capability of the multi-label learner. In order to overcome these two problems, in this paper, we propose a K-labelsets ensemble method based on mutual information and joint entropy. First, the mutual information and joint entropy are adopted to evaluate the redundancy level and imbalance level of each K-labelset. Then, disjoint sampling is performed iteratively, where during each iteration, a number of K-labelsets with low mutual information are retained to be the candidates, and the one with the highest joint entropy is selected. Afterwards, for each selected K-labelset, the label powerset method is employed and a multi-class classification model is constructed. Finally, the multi-class models on different K-labelsets are integrated, and a voting based ensemble model is generated to perform the predictions for unseen samples. We conduct extensive experiments on real-world multi-label data sets. Experimental results demonstrate the effectiveness of the proposed method. <s> BIB004
Spatiotemporal data imbalance is mainly manifested in two aspects: data distribution imbalance and data label imbalance. First of all, for the problem of imbalanced data distribution, Zheng et al. BIB001 proposed a semi-supervised learning algorithm to deal with the problem of sparse training data caused by the lack of air monitoring stations. Then, for the problem of imbalanced data label, Beckmann et al. BIB002 studied a KNN-based undersampling methods for data balancing. Wang et al. BIB004 used a K-labelsets ensemble method based on mutual information and joint entropy to deal with inblanced data. Gong et al. BIB003 presented a ensemble method using random undersampling and ROSE sampling to solve the imbalance classification problem. So when we face the data imbalance problem, it's a good choice to determine data distribution or data label imbalance, and then apply these corresponding methods.