Data are exploding in many fields and may exist in the streaming mode. When the generation speed of massive streaming data far exceeds the processing speed of a single node and the generated data need to be processed in real time, traditional centralized learning models are challenging in meeting the efficiency requirements. Therefore, online distributed learning models emerge. As time progresses, features may continuously emerge from various sources in a distributed and heterogeneous fashion. Therefore, we study the problem of online distributed heterogeneous streaming feature selection and propose a novel framework to address it, named DHSFS. The framework comprises two main components: sub-node streaming feature selection and global information synchronization. The sub-node component uses a dynamic strategy to select strong features, discard irrelevant ones, and cache weakly relevant features. In the global information synchronization stage, each sub-node synchronizes statistics information with the master node to adjust the global thresholds dynamically. Finally, the features selected by each sub-node are summarized and output. Experiments on 16 datasets show that the DHSFS framework has both high prediction accuracy and high efficiency of online stream feature selection.

Conditional Independence (CI) tests play an essential role in causal discovery from observational data, enabling the measurement of independence between two nodes. However, traditional CI tests ignore the imbalanced occurrence probabilities of node values, which may affect the accuracy of determining independence between nodes. To address this problem, we first introduce a new concept of the Node-imbalance phenomenon to describe the imbalance of node values in the Bayesian network data and analyze the influence of the Node-imbalance phenomenon on the traditional CI tests, then we propose a Weight-Based Conditional Independence (WCI) test to improve the accuracy of CI tests in the presence of Node-imbalance. In the experiments, we verify that WCI effectively measures the dependency between nodes in the Node-imbalance phenomenon compared with the traditional independence tests, and the state-of-the-art causal discovery algorithms reduce the number of false causal orientations through WCI.

Feature selection is an essential pre-process component in data mining that aims to select the most relevant features from the target dataset. Datasets are always dynamic in real-world applications, and features may exist in stream mode. Then, online streaming feature selection methods are proposed, which deal with streaming features arriving continuously in real-time. However, most existing algorithms prioritize high accuracy and low time-consumption but overlook the stability of the selected features. Stable feature selection results are crucial for users in practice. For instance, in the medical field, unstable feature selection results can make it challenging for experts to identify the main causative factors of a disease. Motivated by this, this article proposes a new online stable streaming feature selection method via feature aggregation named OSSFS. Specifically, inspired by the cohesive MeanShift approach, OSSFS applies an incremental aggregation strategy to partition the streaming features into multiple hyperellipsoids. Then, we incrementally update and merge these hyperellipsoids with new streaming features. Finally, we select representative features from each hyperellipsoid as the final selected feature subset. Extensive experiments are conducted on several real-world datasets to compare our new method with state-of-the-art competing algorithms in cases of stability and predictive accuracy. Experimental results indicate that OSSFS achieves optimal stability without losing prediction accuracy.

The explosion of data volume has gradually transformed big data processing from the static batch mode to the online streaming model. Streaming data can be divided into instance streams (feature space remains fixed while instances increase over time), feature streams (instance space is fixed while features arrive over time), or both. Generally, online streaming data learning has two main challenges: infinite length and concept changing. Recently, feature stream learning has received much attention. However, existing feature stream learning methods focus on feature selection or classification but ignore the concept changing over time. To the best of our knowledge, this is the first work that studies concept evolution detection over feature streams. Specifically, we first give the formal definition of concept evolution over feature streams, which include three different types: concept emerging, concept drift, and concept forgetting. Then, we design a novel framework to detect the concept evolution over feature streams that consists of a sliding window, an improved density peak-based clustering algorithm, and a weighted bipartite graph-based concept detecting method. Extensive experiments have been conducted on several synthetic and high-dimensional datasets to indicate our new method’s ability to cluster and detect concept evolution over feature streams.

Feature selection is one of the core concepts in machine learning, which hugely impacts the model’s performance. For some real-world applications, features may exist in a stream mode that arrives one by one over time, while we cannot know the exact number of features before learning. Online streaming feature selection aims at selecting optimal stream features at each timestamp on the fly. Without the global information of the entire feature space, most of the existing methods select stream features in terms of individual feature information or the comparison of features in pairs. This article proposes a new online scalable streaming feature selection framework from the dynamic decision perspective that is scalable on running time and selected features by dynamic threshold adjustment. Regarding the philosophy of “Thinking-in-Threes”, we classify each new arrival feature as selecting, discarding, or delaying, aiming at minimizing the overall decision risks. With the dynamic updating of global statistical information, we add the selecting features into the candidate feature subset, ignore the discarding features, cache the delaying features into the undetermined feature subset, and wait for more information. Meanwhile, we perform the redundancy analysis for the candidate features and uncertainty analysis for the undetermined features. Extensive experiments on eleven real-world datasets demonstrate the efficiency and scalability of our new framework compared with state-of-the-art algorithms.

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 Knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.