As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs ; 2) how to select useful NSPs ; and 3) how to reduce high time consumption . To solve the first problem, we propose an algorithm Topk-NSP to mine the ${k}$ most frequent negative patterns. In Topk-NSP, we first mine the top- ${k}$ PSPs using the existing methods, and then we use an idea which is similar to top- ${k}$ PSPs mining to mine the top- ${k}$ NSPs from these PSPs. To solve the remaining two problems, we propose three optimization strategies for Topk-NSP. The first optimization strategy is that, in order to consider the influence of PSPs when selecting useful top- ${k}$ NSPs, we introduce two weights, $w_{P}$ and $w_{N}$ , to express the user preference degree for NSPs and PSPs, respectively, and select useful NSPs by a weighted support wsup . The second optimization strategy is to merge wsup and an interestingness metric to select more useful NSPs. The third optimization strategy is to introduce a pruning strategy to reduce the high computational costs of Topk-NSP. Finally, we propose an optimization algorithm Topk-NSP ⁺ . To the best of our knowledge, Topk-NSP ⁺ is the first algorithm that can mine the top- ${k}$ useful NSPs. The experimental results on four synthetic and two real-life data sets show that the Topk-NSP ⁺ is very efficient in mining the top- ${k}$ NSPs in the sense of computational cost and scalability.

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

The reliable implementation of software in an Internet system directly influences information transmission especially for the Internet of Things (IoT) system. Once defects in the system are found, the communication between things and things and the interaction between people and things in the IoT will be greatly affected. Therefore, rapid and effective defect assignment to the right developer is the key to ensuring software quality and bringing down time consumption in the IoT software life cycle. However, as the size of the software becomes increasing larger, the requirement of users grows, and a large number of software bugs will be found every day. It is difficult for managers to assign the software defects to the appropriate developers. In this paper, a novel hybrid method based on a diversified feature selection and an ensemble extreme learning machine (ELM) is proposed. First, the useful information is extracted from defect reports; then, the data are preprocessed to establish a vector space model; and the diversified feature selection is preprocessed in order to select a smallest set of representative non-redundant features with maximal statistical information. Finally, an ensemble GA-based ELM training classifier is used. Experimental results show that, compared to SVM, C4.5, NaiveBayes, and KNN classifiers, the proposed ELM-based bug triage approach with representative feature selection techniques in this paper significantly improves the efficiency and the effectiveness of bug triages.

Feature selection is an effective dimensionality reduction technique, which can speed up an algorithm and improve model performance such as predictive accuracy and result comprehensibility. The study of selecting label-specific features for each class label has attracted considerable attention since each class label might be determined by some inherent characteristics, where precise label information is required to guide label-specific feature selection. However, obtaining noise-free labels is quite difficult and impractical. In reality, each instance is often annotated by a candidate label set that comprises multiple ground-truth labels and other false-positive labels, termed partial multilabel (PML) learning scenario. Here, false-positive labels concealed in a candidate label set might induce the selection of false label-specific features while masking the intrinsic label correlations, which misleads the selection of relevant features and compromises the selection performance. To address this issue, a novel two-stage partial multilabel feature selection (PMLFS) approach is proposed, which elicits credible labels to guide accurate label-specific feature selection. First, the label confidence matrix is learned to help elicit ground-truth labels from the candidate label set via the label structure reconstruction strategy, each element of which indicates how likely a class label is ground truth. After that, based on distilled credible labels, a joint selection model, including label-specific feature learner and common feature learner, is designed to learn accurate label-specific features to each class label and common features for all class labels. Besides, label correlations are fused into the features selection process to facilitate the generation of an optimal feature subset. Extensive experimental results clearly validate the superiority of the proposed approach.