An adaptive feature fusion framework for multi-class classification based on SVM

An adaptive feature fusion framework is proposed for multi-class classification based on SVM. In a similar manner of one-versus-all (OVA), one of the multi-class SVM schemes, the proposed approach decomposes a multi-class classification into several binary classifications. The main difference lies in that each classifier is created with the most suitable feature vectors to discriminate one class from all the other classes. The feature vectors of the unknown samples are selected by each classifier adaptively such that recognition is fulfilled accordingly. In addition, novel evaluation criterions are defined to deal with the frequent small-number sample problems. A writer recognition experiment is carried out to accomplish this framework with three kinds of feature vectors: texture, structure and morphological features. Finally, the performance of the proposed approach is illustrated as compared with the OVA by applying the same feature vectors for all classes.     

基于稀疏表示与小波特征的人脸识别分层框架

基于稀疏表示的人脸识别研究,非线性特征的选择研究较少。提出分层使用人脸图像的小波特征,进行稀疏表示人脸识别框架。框架首先对样本人脸进行小波变换,构造小波低频和小波高频过完备人脸字典;识别阶段首先使用人脸图像的小波低频特征进行稀疏表示,计算类别模糊稀疏,然后根据模糊系数输出类别标签或进行高频特征的稀疏表示与识别。实验结果表明,基于小波特征和稀疏表示的人脸识别分层框架提高了识别的准确率,且对遮挡很鲁棒。     

An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine

With the development and popularization of the remote-sensing imaging technology, there are more and more applications of hyperspectral image classification tasks, such as target detection and land cover investigation. It is a very challenging issue of urgent importance to select a minimal and effective subset from those mass of bands. This paper proposed a hybrid feature selection strategy based on genetic algorithm and support vector machine (GA–SVM), which formed a wrapper to search for the best combination of bands with higher classification accuracy. In addition, band grouping based on conditional mutual information between adjacent bands was utilized to counter for the high correlation between the bands and further reduced the computational cost of the genetic algorithm. During the post-processing phase, the branch and bound algorithm was employed to filter out those irrelevant band groups. Experimental results on two benchmark data sets have shown that the proposed approach is very competitive and effective.     

A comprehensive survey on deep learning based malware detection techniques

Recent theoretical and practical studies have revealed that malware is one of the most harmful threats to the digital world. Malware mitigation techniques have evolved over the years to ensure security. Earlier, several classical methods were used for detecting malware embedded with various features like the signature, heuristic, and others. Traditional malware detection techniques were unable to defeat new generations of malware and their sophisticated obfuscation tactics. Deep Learning is increasingly used in malware detection as DL-based systems outperform conventional malware detection approaches at finding new malware variants. Furthermore, DL-based techniques provide rapid malware prediction with excellent detection rates and analysis of different malware types. Investigating recently proposed Deep Learning-based malware detection systems and their evolution is hence of interest to this work. It offers a thorough analysis of the recently developed DL-based malware detection techniques. Furthermore, current trending malwares are studied and detection techniques of Mobile malware (both Android and iOS), Windows malware, IoT malware, Advanced Persistent Threats (APTs), and Ransomware are precisely reviewed.     

基于NIDS数据源的网络攻击事件分类技术研究

以网络入侵检测系统（NIDS）的数据源为基础,结合协议分析技术,提出了适合于构建网〖JP2〗络攻击事件库和进行攻击仿真的网络攻击事件分类体系,并用Z语言对其进行了形式化描述和证明。     

Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey

The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios.     

An application of MOGW optimization for feature selection in text classification

The Journal of Supercomputing - Due to extensive web applications, sentiment classification (SC) has become a relevant issue of interest among text mining experts. The extensive online reviews...     

Video content authentication techniques: a comprehensive survey

In this digital day and age, we are becoming increasingly dependent on multimedia content, especially digital images and videos, to provide a reliable proof of occurrence of events. However, the availability of several sophisticated yet easy-to-use content editing software has led to great concern regarding the trustworthiness of such content. Consequently, over the past few years, visual media forensics has emerged as an indispensable research field, which basically deals with development of tools and techniques that help determine whether or not the digital content under consideration is authentic, i.e., an actual, unaltered representation of reality. Over the last two decades, this research field has demonstrated tremendous growth and innovation. This paper presents a comprehensive and scrutinizing bibliography addressing the published literature in the field of passive-blind video content authentication, with primary focus on forgery/tamper detection, video re-capture and phylogeny detection, and video anti-forensics and counter anti-forensics. Moreover, the paper intimately analyzes the research gaps found in the literature, provides worthy insight into the areas, where the contemporary research is lacking, and suggests certain courses of action that could assist developers and future researchers explore new avenues in the domain of video forensics. Our objective is to provide an overview suitable for both the researchers and practitioners already working in the field of digital video forensics, and for those researchers and general enthusiasts who are new to this field and are not yet completely equipped to assimilate the detailed and complicated technical aspects of video forensics.     

gMLC: a multi-label feature selection framework for graph classification

Graph classification has been showing critical importance in a wide variety of applications, e.g. drug activity predictions and toxicology analysis. Current research on graph classification focuses on single-label settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously. Extracting good features using multiple labels of the graphs becomes an important step before graph classification. In this paper, we study the problem of multi-label feature selection for graph classification and propose a novel solution, called gMLC, to efficiently search for optimal subgraph features for graph objects with multiple labels. Different from existing feature selection methods in vector spaces that assume the feature set is given, we perform multi-label feature selection for graph data in a progressive way together with the subgraph feature mining process. We derive an evaluation criterion to estimate the dependence between subgraph features and multiple labels of graphs. Then, a branch-and-bound algorithm is proposed to efficiently search for optimal subgraph features by judiciously pruning the subgraph search space using multiple labels. Empirical studies demonstrate that our feature selection approach can effectively boost multi-label graph classification performances and is more efficient by pruning the subgraph search space using multiple labels.     

An approach for feature selection using local searching and global optimization techniques

Neural Computing and Applications - Classification problems such as gene expression array analysis, text processing of Internet document, combinatorial chemistry, software defect prediction and...     

An effective and fast iris recognition system based on a combined multiscale feature extraction technique

The randomness of iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex iris image structure and the various sources of intra-class variations result in the difficulty of iris representation. Although, a number of iris recognition methods have been proposed, it has been found that several accurate iris recognition algorithms use multiscale techniques, which provide a well-suited representation for iris recognition. In this paper and after a thorough analysis and summarization, a multiscale edge detection approach has been employed as a pre-processing step to efficiently localize the iris followed by a new feature extraction technique which is based on a combination of some multiscale feature extraction techniques. This combination uses special Gabor filters and wavelet maxima components. Finally, a promising feature vector representation using moment invariants is proposed. This has resulted in a compact and efficient feature vector. In addition, a fast matching scheme based on exclusive OR operation to compute bits similarity is proposed where the result experimentation was carryout out using CASIA database. The experimental results have shown that the proposed system yields attractive performances and could be used for personal identification in an efficient and effective manner and comparable to the best iris recognition algorithm found in the current literature.     

A particle swarm optimization based simultaneous learning framework for clustering and classification

A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed in this paper. Firstly, an improved particle swarm optimization (PSO) is used to partition the training samples, the number of clusters must be given in advance, an automatic clustering algorithm rather than the trial and error is adopted to find the proper number of clusters, and a set of clustering centers is obtained to form classification mechanism. Secondly, in order to exploit more useful local information and get a better optimizing result, a global factor is introduced to the update strategy update strategy of particle in PSO. PSOSLCC has been extensively compared with fuzzy relational classifier (FRC), vector quantization and learning vector quantization (VQ+LVQ3), and radial basis function neural network (RBFNN), a simultaneous learning framework for clustering and classification (SCC) over several real-life datasets, the experimental results indicate that the proposed algorithm not only greatly reduces the time complexity, but also obtains better classification accuracy for most datasets used in this paper. Moreover, PSOSLCC is applied to a real world application, namely texture image segmentation with a good performance obtained, which shows that the proposed algorithm has a potential of classifying the problems with large scale.     

Large scale geospatial data conflation: A feature matching framework based on optimization and divide-and-conquer

Geospatial data conflation is the process of combining two datasets to create a better one. It has received increased research attention due to the emergence of new data sources and the need to combine information from these sources in spatial analyses. Many conflation methods exist to date, ranging from simple ones based on spatial join, to sophisticated methods based on statistics and optimization models. This paper focuses on the optimization-based conflation approach. It treats feature-matching in conflation as an optimization problem of finding a plan to match features in two datasets that minimizes the total discrepancy. Optimization based conflation methods may overcome some limitations of conventional methods, such as sub-optimality and greediness. However, they have often been deemed impractical in day-to-day analysis because they induce high computational costs (especially in combining large geospatial data).In this paper, we demonstrate the feasibility of performing optimization-based conflation for large geographic data in Geographic Information Systems. This is accomplished by utilizing efficient network flow-based conflation models and a divide-and-conquer strategy that allows the conflation models to scale to large data. Experiments show that the network-flow based model achieves average recall and precision rates of 97.7% and 90.8%, respectively in small test areas, and outperforms the traditional assignment problem by about 9% each. For larger data, it took the original network-flow model (without divide-and-conquer) nearly two days to conflate the road network in a portion of Los Angeles area near the LAX international airport. By contrast, the same model can be used to conflate the road networks of the entire Los Angeles County, CA in under 3 h with the divide and conquer strategy.     

An algorithm for clustering based on non-smooth optimization techniques

The problem of cluster analysis is formulated as a problem of non‐smooth, non‐convex optimization, and an algorithm for solving the cluster analysis problem based on non‐smooth optimization techniques is developed. We discuss applications of this algorithm in large databases. Results of numerical experiments are presented to demonstrate the effectiveness of this algorithm.     

基于TNG特征扩展的MLFM-MN短文本分类算法

在海量短文本中由于特征稀疏、数据维度高这一问题,传统的文本分类方法在分类速度和准确率上达不到理想的效果。针对这一问题提出了一种基于Topic N-Gram(TNG)特征扩展的多级模糊最小-最大神经网络(MLFM-MN)短文本分类算法。首先通过使用改进的TNG模型构建一个特征扩展库并对特征进行扩展,该扩展库不仅可以推断单词分布,还可以推断每个主题文本的短语分布;然后根据短文本中的原始特征,计算这些文本的主题倾向,根据主题倾向,从特征扩展库中选择适当的候选词和短语,并将这些候选词和短语放入原始文本中;最后运用MLFM-MN算法对这些扩展的原始文本对象进行分类,并使用精确率、召回率和F1分数来评估分类效果。实验结果表明,本文提出的新型分类算法能够显著提高文本的分类性能。