Support vector machine is a classification model which has been widely used in many nonlinear and high dimensional pattern recognition problems. However, it is inefficient or impracticable to implement support vector machine in dealing with large scale training set due to its computational difficulties as well as the model complexity. In this paper, we study the support vector recognition problem mainly in the context of the reduction methods to reconstruct training set for support vector machine. We focus on the fact of uneven distribution of instances in the vector space to propose an efficient self-adaption instance selection algorithm from the viewpoint of geometry-based method. Also, we conduct an experimental study involving eleven different sizes of datasets from UCI repository for measuring the performance of the proposed algorithm as well as six competitive instance selection algorithms in terms of accuracy, reduction capabilities, and runtime. The extensive experimental results show that the proposed algorithm outperforms most of competitive algorithms due to its high efficiency and efficacy

Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods assume that all of views are complete. However, multi-view data are usually incomplete, i.e., a part of instances are presented on some views but not all views. Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views. In this paper, we propose a complementary and consensus learning-based incomplete multi-view unsupervised feature selection method (C $^{2}$ IMUFS) to address the aforementioned issues. Concretely, C $^{2}$ IMUFS integrates feature selection into an extended weighted non-negative matrix factorization model equipped with adaptive learning of view-weights and a sparse $\ell _{2,p}$ -norm, which can offer better adaptability and flexibility. By the sparse linear combinations of multiple similarity matrices derived from different views, a complementary learning-guided similarity matrix reconstruction model is presented to obtain the complete similarity graph in each view. Furthermore, C $^{2}$ IMUFS learns a consensus clustering indicator matrix across different views and embeds it into a spectral graph term to preserve the local geometric structure. Comprehensive experimental results on real-world datasets demonstrate the effectiveness of C $^{2}$ IMUFS compared with state-of-the-art methods.

A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial‐of‐Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high‐speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI) and zero copy and propose an efficient quasi‐real‐time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high‐speed network environment. Our contributions are summarized as follows: (1) Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2) Use random forest model to extract promising conversation features. (3) Analyze the performance of different classification algorithms. The proposed method is demonstrated by well‐known CTU13 dataset and nonmalicious applications. The experimental results show our conversation‐based detection approach can identify botnet with higher accuracy and lower false positive rate than flow‐based approach.

Defect detection in rail fasteners is crucial for train safety, as defective fasteners can cause derailments and severe safety incidents. However, Existing algorithms often struggle in various real-world scenarios due to challenges such as obscured fasteners, motion blur in images, varying camera angles, and fasteners submerged in water. To address these challenges, we propose a Multi-scale CNN-Transformer Hybrid Network for Rail Fastener Defect Detection (MCHNet-RF2D), specifically designed to identify fastener defects in complex environments. Our approach constructs an efficient CNN block and a multi-scale Vision Transformer block to alternately extract local detail features and global semantic features of the fasteners. These features are seamlessly integrated through multi-scale fusion to enhance defect recognition robustness. By combining comprehensive global recognition with detailed local defect detection, MCHNet-RF2D outperforms existing CNN-Transformer hybrid networks by 2.8% and surpasses current fastener defect detection algorithms by 2.9%. In practical deployment on over 40 trains, our model successfully detected more than 2,000 fastener defects, demonstrating its effectiveness in diverse and challenging conditions.