Online learning for data streams has gained significant attention in recent years. Existing methods impose various limitations on the feature space, classes, and labels of data streams. In contrast, real-world data streams often exhibit dynamic feature spaces, missing or partial feature values, undefined classes, and inaccessible labels. These complexities limit the applicability of current online learning methods to real-world scenarios. To fill the gap, this study explores a new online learning problem and proposes a novel algorithm:OnlineLearning forFickle Data Streams withoutLabels (OLFL). Specifically, OLFL has a triple main idea: 1) It tackles dimensional turbulence through a completion strategy that collaborates with weight vectors, reducing the over-reliance on surviving features; 2) It implements known class classification and new class detection via the density of core points within the neighborhood of each instance; 3) It utilizes a constraint-based adaptive coefficient vector to update model. We evaluate OLFL on extensive real-world and synthetic datasets against strong baselines, covering fickle feature spaces, emerging classes, and varying missingness rates. We further conduct ablation studies and case analyses. The results consistently show that OLFL outperforms the baselines in both accuracy and efficiency.

Online learning is critical for handling complex data streams in Big Data-related applications. This study explores a new online learning problem where both the features and labels are incomplete. Such incompleteness poses a critical challenge in determining the latent relationship between incomplete features and labels. Unfortunately, existing online learning methods only consider a few cases of incomplete feature spaces, such as trapezoidal, evolvable, and capricious data streams, limiting their applicability to this problem. To bridge this gap, this study proposes a novel algorithm ofOnlineLearning for Data Streams withIncompleteFeatures andLabels (OLIFL). OLIFL imposes no constraints on changing patterns of feature space and does not require all instances to be labeled with two-fold ideas. First, OLIFL explores the informativeness of individual features to update the classifier by dynamically maintaining global feature space and updating the informativeness matrix. Second, it estimates the label confidence of unlabeled instances to control their negative effects by limiting the error upper bound. Extensive experiments on benchmark datasets are conducted in five scenarios: three incomplete feature (trapezoidal, evolvable, and capricious) spaces, and two incomplete labels (only missing labels and missing both features and labels). In addition, we explore the sensitivity of the model to parameters, and its usability and response efficiency in handling concept drifts. The results show that OLIFL significantly outperforms its rivals. Moreover, we use OLIFL to classify a movie review task as real application verification.

Multi-label streaming feature selection has attracted extensive attention in diverse big data applications. However, most existing works focused on the scenarios where labels are independent, while ignoring the real scenarios that they may be interdependent and correlated with each other. This paper aims to fill this gap by developing a novel online multi-label streaming feature selection scheme by taking into account the existence of label correlation, known as (OMSFSLC). In our design, we first calculate the correlation degree between labels to obtain the label weight. Then, we integrate the mutual information and the label weight to evaluate the correlation between features and labels. In particular, it consists of three stages: 1) online significance analysis, which can determine the significant features via the correlation degree between the newly arriving features and labels; 2) online relevance analysis, which can obtain relevant features via the mutual information; and 3) online redundancy analysis, which can filter the redundant features for removal via pairwise comparison. We implement our solution and conduct extensive experiments on benchmark datasets for performance evaluations. The experimental results exhibit that OMSFSLCsignificantly outperforms the state-of-the-art methods in terms of effectiveness and efficiency.

Learning with streaming data has attracted extensive research interest in recent years. Existing online learning approaches have specific assumptions regarding data streams, such as requiring fixed or varying feature spaces with explicit patterns and balanced class distributions. While the data streams generated in many real scenarios commonly have arbitrarily incomplete feature spaces and dynamic imbalanced class distributions, making existing approaches be unsuitable for real applications. To address this issue, this paper proposes a novelOnlineLearning fromIncomplete andImbalancedDataStreams (OLI$^{2}$DS) algorithm. OLI$^{2}$DS has a two-fold main idea: 1) it follows the empirical risk minimization principle to identify the most informative features of incomplete feature spaces, and 2) it develops a dynamic cost strategy to handle imbalanced class distributions in real-time by transforming F-measure optimization into a weighted surrogate loss minimization. To evaluate OLI$^{2}$DS, we compare it with state-of-the-art related algorithms in three kinds of experiments. First, we adopt 14 real datasets to simulate three scenarios of incomplete feature spaces, i.e., trapezoidal, feature evolvable, and capricious data streams. Second, based on a benchmark online analyzer, we generate 13 datasets to simulate incomplete data streams with different imbalance ratios. Third, we analyze concept drift in two simulated scenes, i.e., online learning and data stream mining, and verify the adaption of OLI$^{2}$DS on repeated concept drifts and variable imbalance ratios. The results demonstrate that OLI$^{2}$DS achieves a significantly better performance than its rivals. Besides, a real-world case study on movie review classification is conducted to elaborate on our OLI$^{2}$DS algorithm's effectiveness. Code is released athttps://github.com/youdianlong/OLI2DS.