Imbalanced Time-Series Classification is a critical, yet challenging task across a spectrum of real-world applications. Previous oversampling and generative approaches primarily target the minority class and often rely on static decision boundaries or similarity-based heuristics. However, these methods overlook the underlying causal factors that govern the distinction between majority and minority classes, particularly in scenarios with ambiguous class boundaries. As a result, the generated samples may fail to enhance class separability, thereby limiting improvements in classification performance. To this end, we propose a CounterFactual Augmentation Minority Generation (CFAMG) method based on generative models that aims to discover the causal factors that determine different classes from a causality perspective. Specifically, our method first utilizes a disentangled classifier to distinguish between causal and non-causal factors. Next, we perform counterfactual intervention by replacing the causal factors of majority class samples with those from minority class samples, creating an intervened latent representation that reflects minority characteristics while preserving essential structures. Finally, the trained minority class decoder generates counterfactual minority samples that resemble real minority instances yet remain distinguishable from the original majority class. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in both univariate and multivariate imbalanced time-series classification tasks. The code is published at https://github.com/WangLei-CQU/CFAMG.

A significant challenge in air quality index (AQI) prediction is to accurately evaluate the potential outcomes after conducting interventions in pollutant factors such as industrial emissions for each enterprise. Existed methods often suffer from spurious correlations caused by unmeasured confounders and are lack of interpretability of the model, leading to sub-optimal prediction performance. This motivates us to propose a causal AQI prediction framework (CAP) that employs a structural causal model (SCM) to characterize the causal structural variability of various AQI factors for robust AQI prediction. Specifically, we employ the front-door adjustment to explicitly eliminate unmeasured confounders by intervening in industrial emissions from the target enterprise. Meanwhile, we take industrial emissions of neighboring enterprises into account when intervening in the target enterprise and simulate the dispersion of industrial emissions through a Gaussian plume model based on meteorological factors. Experiments on two real-world datasets validate the superior performance of our model on AQI prediction compared to the state-of-the-art baselines.

In database of recommender systems, users' ratings for most items are usually missing, resulting in selection bias when users selectively choose items to rate. To address this problem, propensity-based methods, e.g., inverse propensity scoring and doubly robust, have been widely studied and applied to missing rating prediction and post-click conversion rate prediction tasks. However, have we completely eliminated the selection bias? Under what missing data mechanism can previous studies completely eliminate the selection bias and lead to unbiased learning? In this paper, following the previous literature on statistics, we first formally define three missing data mechanisms, i.e., missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), and discuss the widespread prevalence of MNAR in recommender systems. Next, we theoretically reveal that the unbiasedness of previous propensity-based debiasing methods is valid only when data are MCAR or MAR, while it leads to biased predictions when data are MNAR. To tackle this research gap, we propose to disentangle user and item embeddings into the primary latent vector for rating prediction and the auxiliary latent vector for missing mechanism modeling. We prove the identifiablility results, and show that the proposed method can achieve unbiased learning under MNAR with imposed constraints. Extensive experiments are conducted on a semi-synthetic dataset and three real-world datasets, validating the effectiveness of our proposed method.

Partial multi-label learning (PML) deals with the problem where each training example is assigned multiple candidate labels, only a part of which are correct. To learn from such PML examples, the straightforward model training tends to be misled by the noise candidate label set. To alleviate this problem, a coupled framework is established in this paper to learn the desired model and perform the relabeling procedure alternatively. In the relabeling procedure, instead of simply extracting relative label confidences, or deterministically eliminating low confidence labels and preserving high confidence labels as ground-truth ones, we introduce a soft sign thresholding operator to adaptively strengthen candidate labels with high confidence and weaken candidate labels with low confidence, which enlarges the difference of confidences of candidate labels within allowable range. We further show that the resulting nonconvex quadratic programming (QP) optimization problem can be relaxed into a convex QP problem with proper conditions. Extensive experiments on synthesized and real-world data sets demonstrate the effectiveness of our proposed approach.

Existing topic models for mining sentiments from articles often ignores the fact that intra-topic correlations are common and useful to uncover a large number of fine-grained and tightly-coherent topics. This paper is concerned with the problem of social sentiment mining by modeling topic correlations. We aim to not only discover the connections between sentiments and topics, but also reveal the deeper relationship among topics where some topics may co-occur more frequently than others in articles. More specifically, we join sentiment mining with hierarchical pachinko allocation model to represent topic correlations by a hierarchy. In our model, the hierarchical pachinko allocation is employed to generate the latent hierarchical topic variables and sentiment variables. Experimental results on a collected news corpus show that our model can effectively identify latent topics in a hierarchical structure, and outperforms competing sentiment-topic models such as Latent Dirichlet Allocation based model in sentiment prediction.

In today’s race walking competition, the determination of whether an athlete fouls is mainly affected by a referee’s subjective judgment, leading to a high possibility of misjudgment. The purpose of this work is to determine whether race walking can be automatically recognized by accelerometers embedded in smartphones. In this work, acceleration data are collected by a smartphone app developed by ourselves. Nineteen features are extracted from the raw sensor data, and are used by an unsupervised classification method for activity recognition, named MCODE. We evaluate various data sampling rates and window lengths during feature extraction in the experiments. We also compare our method with other well-known methods on the metrics such as sensitivity, specificity and adjusted rank index. The results show that our method is viable to recognize race walking using smartphone accelerometers.