ACM Transactions on Knowledge Discovery from Data最新文献

High utility itemsets (HUIs) mining is an emerging area of data mining which discovers sets of items generating a high profit from transactional datasets. In recent years, several algorithms have been proposed for this task. However, most of them do not consider the on-shelf time period of items and negative utility of items. High on-shelf utility itemset (HOUIs) mining is more difficult than traditional HUIs mining because it deals with on-shelf based time period and negative utility of items. Moreover, most algorithms need minimum utility threshold ((min_util )) to find rules. However, specifying the appropriate (min_util ) threshold is a difficult problem for users. A smaller (min_util ) threshold may generate too many rules and a higher one may generate a few rules, which can degrade performance. To address these issues, a novel top-k HOUIs mining algorithm named TKOS (Top-K high On-Shelf utility itemsets miner) is proposed which considers on-shelf time period and negative utility. TKOS presents a novel branch and bound based strategy to raise the internal (min_util ) threshold efficiently. It also presents two pruning strategies to speed up the mining process. In order to reduce the dataset scanning cost, we utilize transaction merging and dataset projection techniques. Extensive experiments have been conducted on real and synthetic datasets having various characteristics. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms. The proposed algorithm is up to 42 times faster and uses up-to 19 times less memory compared to the state-of-the-art KOSHU. Moreover, the proposed algorithm has excellent scalability in terms of time periods and the number of transactions.

In recent years, Graph Neural Networks (GNNs) have achieved unprecedented success in handling graph-structured data, thereby driving the development of numerous GNN-oriented techniques for inductive knowledge graph completion (KGC). A key limitation of existing methods, however, is their dependence on pre-defined aggregation functions, which lack the adaptability to diverse data, resulting in suboptimal performance on established benchmarks. Another challenge arises from the exponential increase in irrelated entities as the reasoning path lengthens, introducing unwarranted noise and consequently diminishing the model’s generalization capabilities. To surmount these obstacles, we design an innovative framework that synergizes Multi-Level Sampling with an Adaptive Aggregation mechanism (MLSAA). Distinctively, our model couples GNNs with enhanced set transformers, enabling dynamic selection of the most appropriate aggregation function tailored to specific datasets and tasks. This adaptability significantly boosts both the model’s flexibility and its expressive capacity. Additionally, we unveil a unique sampling strategy designed to selectively filter irrelevant entities, while retaining potentially beneficial targets throughout the reasoning process. We undertake an exhaustive evaluation of our novel inductive KGC method across three pivotal benchmark datasets and the experimental results corroborate the efficacy of MLSAA.

Early classification of longitudinal data remains an active area of research today. The complexity of these datasets and the high rates of missing data caused by irregular sampling present data-level challenges for the Early Longitudinal Data Classification (ELDC) problem. Coupled with the algorithmic challenge of optimising the opposing objectives of early classification (i.e., earliness and accuracy), ELDC becomes a non-trivial task. Inspired by the generative power and utility of the Generative Adversarial Network (GAN), we propose a novel context-conditional, longitudinal early classifier GAN (LEC-GAN). This model utilises informative missingness, static features, and earlier observations to improve the ELDC objective. It achieves this by incorporating ELDC as an auxiliary task within an imputation optimization process. Our experiments on several datasets demonstrate that LEC-GAN outperforms all relevant baselines in terms of F1 scores while increasing the earliness of prediction.

Multi-instance Learning (MIL) is a popular learning paradigm arising from many real applications. It assigns a label to a set of instances, named as a bag, and the bag’s label is determined by the instances within it. A bag is positive if and only if it has at least one positive instance. Since labeling bags is more complicated than labeling each instance, we will often face the mislabeling problem in MIL. Furthermore, it is more common that a negative bag has been mislabeled to a positive one since one mislabeled instance will lead to the change of the whole bag label. This is an important problem that originated from real applications, e.g., web mining and image classification, but little research has concentrated on it as far as we know. In this paper, we focus on this MIL problem with one side label noise that the negative bags are mislabeled as positive ones. To address this challenging problem, we propose a novel multi-instance learning method with One Side Label Noise (OSLN). We design a new double weighting approach under traditional framework to characterize the ’faithfulness’ of each instance and each bag in learning the classifier. Briefly, on the instance level, we employ a sparse weighting method to select the key instances, and the MIL problem with one size label noise is converted to a mislabeled supervised learning scenario. On the bag level, the weights of bags, together with the selected key instances, will be utilized to identify the real positive bags. In addition, we have solved our proposed model by an alternative iteration method with proved convergence behavior. Empirical studies on various datasets have validated the effectiveness of our method.

A formidable challenge in the multi-label text classification (MLTC) context is that the labels often exhibit a long-tailed distribution, which typically prevents deep MLTC models from obtaining satisfactory performance. To alleviate this problem, most existing solutions attempt to improve tail performance by means of sampling or introducing extra knowledge. Data-rich labels, though more trustworthy, have not received the attention they deserve. In this work, we propose a multiple-stage training framework to exploit both model- and feature-level knowledge from the head labels, to improve both the representation and generalization ability of MLTC models. Moreover, we theoretically prove the superiority of our framework design over other alternatives. Comprehensive experiments on widely-used MLTC datasets clearly demonstrate that the proposed framework achieves highly superior results to state-of-the-art methods, highlighting the value of head labels in MLTC.

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%.

In graph classification, attention- and pooling-based graph neural networks (GNNs) predominate to extract salient features from the input graph and support the prediction. They mostly follow the paradigm of “learning to attend”, which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm causes GNN classifiers to indiscriminately absorb all statistical correlations between input features and labels in the training data, without distinguishing the causal and noncausal effects of features. Rather than emphasizing causal features, the attended graphs tend to rely on noncausal features as shortcuts to predictions. These shortcut features may easily change outside the training distribution, thereby leading to poor generalization for GNN classifiers. In this paper, we take a causal view on GNN modeling. Under our causal assumption, the shortcut feature serves as a confounder between the causal feature and prediction. It misleads the classifier into learning spurious correlations that facilitate prediction in in-distribution (ID) test evaluation, while causing significant performance drop in out-of-distribution (OOD) test data. To address this issue, we employ the backdoor adjustment from causal theory — combining each causal feature with various shortcut features, to identify causal patterns and mitigate the confounding effect. Specifically, we employ attention modules to estimate the causal and shortcut features of the input graph. Then, a memory bank collects the estimated shortcut features, enhancing the diversity of shortcut features for combination. Simultaneously, we apply the prototype strategy to improve the consistency of intra-class causal features. We term our method as CAL+, which can promote stable relationships between causal estimation and prediction, regardless of distribution changes. Extensive experiments on synthetic and real-world OOD benchmarks demonstrate our method’s effectiveness in improving OOD generalization. Our codes are released at https://github.com/shuyao-wang/CAL-plus.

Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy of fair feature identification. To address this issue, we propose a Fair Causal Feature Selection algorithm, called FairCFS. Specifically, FairCFS constructs a localized causal graph that identifies the Markov blankets of class and sensitive variables, to block the transmission of sensitive information for selecting fair causal features. Extensive experiments on seven public real-world datasets validate that FairCFS has comparable accuracy compared to eight state-of-the-art feature selection algorithms, while presenting more superior fairness.

Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert low weights on samples which are likely to be noisy or quite hard. This study summarizes another less-explored strategy, namely, perturbation. Various incarnations of perturbation have been utilized but it has not been explicitly revealed. Learning with perturbation is called perturbation learning and a systematic taxonomy is constructed for it in this study. In our taxonomy, learning with perturbation is divided on the basis of the perturbation targets, directions, inference manners, and granularity levels. Many existing learning algorithms including some classical ones can be understood with the constructed taxonomy. Alternatively, these algorithms share the same component, namely, perturbation in their procedures. Furthermore, a family of new learning algorithms can be obtained by varying existing learning algorithms with our taxonomy. Specifically, three concrete new learning algorithms are proposed for robust machine learning. Extensive experiments on image classification and text sentiment analysis verify the effectiveness of the three new algorithms. Learning with perturbation can also be used in other various learning scenarios, such as imbalanced learning, clustering, regression, and so on.