Software defect prediction (SDP) is a primary field of study in software engineering, aiming to optimize test resource allocation by highlighting the defect-prone software modules. Over the last few years, ensemble learning method has been extensively adopted in SDP. However, how to strengthen the diversity of base learners is an issue in ensemble learning. In this paper, we consider the problem of diversity in the eye of feature space perturbation. First, we propose the notion of neighborhood granularity discrimination index (NGDI), by combining the neighborhood knowledge granularity with the neighborhood discrimination index within the framework of neighborhood rough sets. NGDI can not only measure the uncertainty of feature subsets' discriminant capability, but also characterize the granularity of neighborhood knowledge induced by feature subsets. Second, we propose an ensemble learning algorithm, EL-NGDI, established on the NGDI. ELNGDI disturbs the feature space using multiple NGDI-based neighborhood approximate reducts. Third, we use ELNGDI to predict software defects. ELNGDI and the Synthetic Minority Oversampling Technique (SMOTE) are combined in order to handle the class imbalance issue in SDP, and propose a mechanism called SMOTE-ELNGDI. Experimental results on 20 datasets demonstrate that ELNGDI effectively improves the performance of SDP compared with existing ensemble learning methods.

Disentanglement techniques used in collaborative filtering uncover interaction intents between nodes, improving the interpretability of node representations and enhancing recommendation performance. However, existing disentanglement methods still face the following two problems. 1) They focus on local structural features derived from direct node interactions, overlooking the comprehensive graph structure, which limits disentanglement accuracy. 2) The disentanglement process depends on backpropagation signals derived from recommendation tasks, lacking direct supervision, which may lead to biases and overfitting. To address the issues, we propose theIntentPropagationContrastiveCollaborativeFiltering (IPCCF) algorithm. Specifically, we design a double helix message propagation framework to more effectively extract the deep semantic information of nodes, thereby improving the model's understanding of interactions between nodes. An intent message propagation method is also developed that incorporates graph structure information into the disentanglement process, thereby expanding the consideration scope of disentanglement. In addition, contrastive learning techniques are employed to align node representations derived from the structure and intents, providing direct supervision for the disentanglement process, mitigating biases, and enhancing the model's robustness to overfitting. The experiments on three real data graphs illustrate the superiority of the proposed approach.

The purpose of Intrusion Detection Systems (IDS) is to identify security issues in data transmitted by various devices and communication protocols. For domains with sparse data, such as the Internet of Things (IoT), cross-domain models are applied to solve the sparse problem by transfer knowledge from the source domain with rich data to the target domain. However, most of the cross-domain intrusion detection methods map different explicit features in the source and target domains to implicit features in a common implicit space, which weakens the interpretability of these methods. To enhance the interpretability of cross-domain models, we propose a Cross-Domain Intrusion Detection Method Based on Nonlinear Augmented Explicit Features (NAEF). Specifically, we augment the feature space of the source and target domains as the combination of shared features, source domain specific features and target domain specific features. Moreover, we model the nonlinear mapping relationship from shared features to special features in the source and target domains separately. Then, the original features in the source and target domains are mapped to uniform explicit features in the augmented space by migration of the nonlinear mapping relationship. Additionally, a classifier based on ensemble learning and attention mechanism balances the data distribution and selects important features to enhance detection performance. Our experimental results demonstrate the effectiveness of the proposed NAEF method on four public datasets.

Bipartite graph representation learning embeds users and items into a low-dimensional latent space based on observed interactions. Previous studies mainly fall into two categories: one reconstructs the structural relations of the graph through the representations of nodes, while the other aggregates neighboring node information using graph neural networks. However, existing methods only explore the local structural information of nodes during the learning process. This makes it difficult to represent the macroscopic structural information and leaves it easily affected by data sparsity and noise. To address this issue, we propose the Intent Distribution based Bipartite graph Representation learning (IDBR) model, which explicitly integrates node intent distribution information into the representation learning process. Specifically, we obtain node intent distributions through clustering and design an intent distribution based graph convolution neural network to generate node representations. Compared to traditional methods, we expand the scope of node representations, enabling us to obtain more comprehensive representations of global intent. When constructing the intent distributions, we effectively alleviated the issues of data sparsity and noise. Additionally, we enrich the representations of nodes by integrating potential neighboring nodes from both structural and semantic dimensions. Experiments on the link prediction and recommendation tasks illustrate that the proposed approach outperforms existing state-of-the-art methods. The code of IDBR is available at https://github.com/rookitkitlee/IDBR.

Abstract Generally, open source software (OSS) has a longer bug‐fixing time. If the bug‐fixing time can be predicted accurately as early as possible, it will be beneficial to the efficiency of bug fixing. Traditional bug‐fixing time prediction models are usually based on static features of bug report. It is difficult to go into service due to inappropriate feature extraction of data and low prediction accuracy of models. The HMM prediction model can predict the bug‐fixing time accurately according to earlier fixing activities. However, this method of temporal sequence feature selection results in a large number of inconsistent samples, and the HMM prediction model can only capture the adjacent activity behavior information of one sequence, and hence, it will reduce the performance of bug‐fixing time prediction. By incorporating the activity information and time information of bug activity transfer, the proportion of inconsistent samples is reduced significantly. In this paper, a double‐sequence input LSTM model (LSTM‐DA) is designed to capture both sequences interaction features and long‐distance‐dependent features. The results of the experiments show that the proposed model can improve the F‐measure and accuracy indicators by about 10% compared with the HMM model in all dimensions, which demonstrates the effectiveness of our method.

Ensemble learning is an effective approach for improving the generalization ability of base classifiers. To generate a set of accurate and diverse base classifiers, different data perturbation schemes have been proposed. For instance, Bagging perturbs the training data via bootstrap sampling. However, when a stable learning algorithm (e.g., KNN, Naive Bayes) is used to train base classifiers, the sole perturbation on the training data may not produce diverse base classifiers. In this paper, by using the attribute reduction technology in rough sets, a multi-modal perturbation-based algorithm (called ‘E _ EARBS’) is proposed for the ensemble of base classifiers. E _ EARBS simultaneously perturbs the feature space, training data and learning parameters, where the relative decision entropy(RDE)-based approximate reducts are used to perturb the feature space, and bootstrap sampling is used to perturb the training data. Experimental results show that E _ EARBS can provide competitive solutions for ensemble learning.

In mobile computing, machine learning models for natural language processing (NLP) have become one of the most attractive focus areas in research. Association rules among attributes are common knowledge patterns, which can often provide potential and useful information such as mobile users′ interests. Actually, almost each attribute is associated with a hierarchy of the domain. Given an relation R = (U, A) and any cut αa on the hierarchy for every attribute a, there is another rough relation RΦ, where Φ = (αa : a ∈ A). This paper will establish the connection between the functional dependencies in R and RΦ, propose the method for extracting reducts in RΦ, and demonstrate the implementation of proposed method on an application in data mining of association rules. The method for acquiring association rules consists of the following three steps: (1) translating natural texts into relations, by NLP; (2) translating relations into rough ones, by attributes analysis or fuzzy k‐means (FKM) clustering; and (3) extracting association rules from concept lattices, by formal concept analysis (FCA). Our experimental results show that the proposed methods, which can be applied directly to regular mobile data such as healthcare data, improved quality, and relevance of rules.

The traditional relational database model (RDM) is not effective for dealing with imprecise and uncertain data as it deals with precise and unambiguous data. Hence, Beaubouef et al. proposed the rough relational database model (RRDM) for the management of uncertainty in relational databases. Beaubouef et al. defined the corresponding rough relational operators in rough relational databases as in ordinary relational databases. And to give an effective measure of uncertainty in rough relational databases, they defined the rough relation entropy. In this paper, we further discuss the issues of relational operations and uncertainty measure in rough relational databases. We give some new definitions for rough relational operators and rough relation entropy in rough relational databases. Furthermore, we discuss the basic properties of rough relational operators and rough relation entropy, as well as the connections between rough relational operators and rough relation entropy.