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.     

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.     

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...     

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.     

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.     

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.     

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.     

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.     

An adaptive boosting algorithm based on weighted feature selection and category classification confidence

Applied Intelligence - Adaptive boosting (Adaboost) is a typical ensemble learning algorithm, which has been studied and widely used in classification tasks. Traditional Adaboost algorithms ignore...     

Synergies between texture features: an abstract feature based framework for meningioma subtypes classification

Histopathology is the gold standard for accurate diagnosis of cancer, tumors and similar diseases. Real-world pathological images, due to non-homogeneous nature and unorganized spatial intensity variations, are complex to analyze and classify. The major challenge in classifying pathological images is the complexity due to high intra-class variability and low inter-class variation in texture. Accuracy of histopathological image classification is highly dependent on the relevancy of the selected features to the problem. This paper is an effort in the same direction and presents an abstract feature based framework called abstract feature framework (AFF) to select optimal set of the most relevant features to classify pathological images. An abstract feature is created by identifying interlinked run-length texture features and grouping them. AFF is comprised of a new data structure called Abstract Feature Tree (AFT) and an algorithm for manipulating it. AFT is a tree structure in which nodes are abstract features. The Linkage Learning Algorithm for manipulating AFT is the brain of this framework and inspired by genetic algorithm. It creates better abstract features by first identifying interlinked abstract features and then combining them. This process is repeated until no improvement is found. On termination, the final list of abstract features is used for classifying pathological images. The proposed framework was tested on real-world histopathological meningioma dataset. Results obtained proved that the proposed framework outperformed the best-known rank-based feature selection techniques by using, on average, approximately three times less features to achieve 22% higher classification accuracy.     

Product modeling framework based on interaction feature pair

Designing products effectively and efficiently is of great significance. However, currently part models are usually created without knowing how they will interact with other parts, leading to gaps between part modeling and assembly modeling and between other applications, and to tedious and redundant labor. This paper proposes a novel product modeling framework to address the problems. The framework is different from current product modeling systems from two aspects. On the architecture level, a new module based on a concept of interaction feature pair (IFP) is developed. An IFP incorporates information of interaction type, related feature pairs and behavioral information that fulfill the interactions. The new module can model the structure of IFPs mathematically through operators and functions defined in a space spanned from six basic IFPs. It can also utilize the constituent elements of an IFP as state variables to form behavior models for the IFP. On the process level, the IFP-based framework can support both bottom-up and top-down approaches, and integrate part modeling and assembly modeling together by changing the workflows. Concretely, IFPs will be embedded into part models at part modeling stage to make them pre-interact with each other, and at assembly modeling stage, parts will be assembled by instantiating the embedded IFPs instead of specifying mating constraints, thus reducing the tedious and redundant labor. Incorporating knowledge of different domains, IFPs can also be developed to integrate more applications together. The implementation of the framework is demonstrated through a prototype system.     

Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework

Structural and Multidisciplinary Optimization - Reliability-based design optimization (RBDO) is an active field of research with an ever increasing number of contributions. Numerous methods have...     

A novel gaussian based particle swarm optimization gravitational search algorithm for feature selection and classification

Neural Computing and Applications - A Gaussian based Particle Swarm Optimization Gravitational Search Algorithm (GPSOGSA) is being proposed for extensive feature selection that serves highly in...