In real-world graph data, in addition to the class imbalance problem, there also exists topological imbalance. However, existing methods mainly focus on addressing class imbalance or only utilize shallow topological information. To address this issue, we first analyze the impact of graph robustness on label propagation and find that local topological robustness, measured by graph efficiency, plays a crucial role in label propagation. Based on this finding, we propose the GLoFT framework, which redefines topological weights by integrating global influence and local topological robustness, effectively addressing the topological imbalance problem. However, the implementation of GLoFT's core idea relies on high-quality labelled data generated by clustering. In partially labelled scenarios, a decline in pseudo-label quality can significantly weaken its effectiveness and even intensify the class imbalance problem. To cope with this challenge, we propose the fusion-based progressive matching for self-training module (FPMist), which is specifically designed for GLoFT, aiming to enhance the quality of pseudo-labels generated by clustering and improve GLoFT's robustness in partially labelled data settings. Experimental results demonstrate that GLoFT + FPMist outperforms existing methods constructed on multiple GNN backbones and in scenarios with severe class imbalance, showing its superior generalization ability and stability.

Fuzzy support vector machine (FSVM) has been combined with class imbalance learning (CIL) strategies to address the problem of classifying skewed data. However, the existing approaches hold several inherent drawbacks, causing the inaccurate prior data distribution estimation, further decreasing the quality of the classification model. To solve this problem, we present a more robust prior data distribution information extraction method named relative density, and two novel FSVM-CIL algorithms based on the relative density information in this paper. In our proposed algorithms, a K-nearest neighbors-based probability density estimation (KNN-PDE) alike strategy is utilized to calculate the relative density of each training instance. In particular, the relative density is irrelevant with the dimensionality of data distribution in feature space, but only reflects the significance of each instance within its class; hence, it is more robust than the absolute distance information. In addition, the relative density can better seize the prior data distribution information, no matter the data distribution is easy or complex. Even for the data with small injunctions or a large class overlap, the relative density information can reflect its details well. We evaluated the proposed algorithms on an amount of synthetic and real-world imbalanced datasets. The results show that our proposed algorithms obviously outperform to some previous work, especially on those datasets with sophisticated distributions.

It is well known that active learning can simultaneously improve the quality of the classification model and decrease the complexity of training instances. However, several previous studies have indicated that the performance of active learning is easily disrupted by an imbalanced data distribution. Some existing imbalanced active learning approaches also suffer from either low performance or high time consumption. To address these problems, this paper describes an efficient solution based on the extreme learning machine (ELM) classification model, called active online-weighted ELM (AOW-ELM). The main contributions of this paper include: 1) the reasons why active learning can be disrupted by an imbalanced instance distribution and its influencing factors are discussed in detail; 2) the hierarchical clustering technique is adopted to select initially labeled instances in order to avoid the missed cluster effect and cold start phenomenon as much as possible; 3) the weighted ELM (WELM) is selected as the base classifier to guarantee the impartiality of instance selection in the procedure of active learning, and an efficient online updated mode of WELM is deduced in theory; and 4) an early stopping criterion that is similar to but more flexible than the margin exhaustion criterion is presented. The experimental results on 32 binary-class data sets with different imbalance ratios demonstrate that the proposed AOW-ELM algorithm is more effective and efficient than several state-of-the-art active learning algorithms that are specifically designed for the class imbalance scenario.

Software quality properties, normally derived from non-functional requirements, are becoming more important for software. A main reason for software evolution is the unsatisfaction to software quality properties. When improving these properties through software evolution, it is essential to know whether software functions are affected and by how much. This paper proposes an approach to linking the functions with the quality properties of software for evolution via software architecture styles, aiming at contributing to (1) predicting evolution efforts and (2) transforming software for improving its quality.

In order to take advantage the Internetware paradigm, software architecture is important research subject. Software architecture is the core of software systems and acts as a guideline for many development activities. Currently, not many approaches support the self-adaptability of architecture in Internetware. In this paper, an approach to evolving the software architecture in Internetware is presented, consisting of (1) representing source architecture via graph description and discussing the impact of qualities and functions on architecture, (2) transforming it into the new style via transformation techniques through the verified rules, (3) regenerating the code of the target architecture through the FermaT workbench, and (4) proposing a runtime supporting evolution environment for Internetware based systems.