Yanpeng Qu

Yanpeng Qu
Dalian Maritime University · School of AI

PhD

About

48
Publications
5,073
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421
Citations
Additional affiliations
November 2012 - present
Dalian Maritime University
Position
  • Professor (Associate)

Publications

Publications (48)
Chapter
Irrelevant features of the dataset have a certain impact on the judgment ability of the classifier. Effectively distinguishing strong and weakly relevant features can improve classification performance. Since neural networks can effectively dig out the underlying impact of the conditional features on the decision, in this work, the knowledge learne...
Article
Full-text available
Conventionally, the k nearest-neighbor (kNN) classification is implemented with the use of the Euclidean distance-based measures, which are mainly the one-to-one similarity relationships such as to lose the connections between different samples. As a strategy to alleviate this issue, the coefficients coded by sparse representation have played a rol...
Article
Attribute reduction (AR) plays an important role in reducing irrelevant and redundant domain attributes, while maintaining the underlying semantics of retained ones. Based on Earth Mover’s Distance (EMD), this paper presents a robust AR algorithm from the perspective of minimising the inconsistency between the discernibility of the reduct and the e...
Chapter
The k-means algorithm is a classic unsupervised learning approach for the clustering problem, which has a good effect on processing spherical data. Since its initial centroids are randomly assigned, the clustering effect is usually not stable. Moreover, the traditional k-means and related methods often only consider the similarity of the instances...
Chapter
Due to the mechanism of the instance-based classification, the feature significance plays an important role in the nearest-neighbour classification tasks. The existence of the irrelevant features would degrade the performance of these algorithms by mischoosing the nearest neighbours. However, these irrelevant features are normally inevitable in the...
Article
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Feature selection plays an important role in reducing irrelevant and redun- dant features, while retaining the underlying semantics of selected ones. An effective feature selection method is expected to result in a significantly reduced subset of the original features without sacrificing the quality of problem-solving (e.g., classification). In thi...
Article
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The use of computer aided diagnosis (CAD) systems, which are computer based tools for the automatic analysis of medical images such as mammogram and prostate MRI, can assist in the early detection and diagnosis of developing cancer. In the process of CAD for mammogram, the task of image processing (IP) plays a fundamental role in providing promisin...
Article
Feature extraction plays a vital role in visual action recognition. Many existing gradient-based feature extractors, including histogram of oriented gradients (HOG), histogram of optical flow (HOF), motion boundary histograms (MBH), and histogram of motion gradients (HMG), build histograms for representing different actions over the spatio-temporal...
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Full-text available
Context and background: Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution. Motivation: Computer aided risk analysis of breast cancer typically works with a se...
Conference Paper
Full-text available
The rapid expansion of the Internet has experienced a significant increase in cases of child abuse, as more and more young children have greater access to the Internet. In particular, adults and minors are able to exchange sexually explicit messages and media via a variety of online platforms that are widely available, which leads to an increasing...
Article
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Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance...
Chapter
Network intrusion is a growing threat with potentially severe impacts, which can be damaging in multiple ways to network infrastructures and digital/intellectual assets in the cyberspace. The approach most commonly employed to combat network intrusion is the development of attack detection systems via machine learning and data mining techniques. Th...
Conference Paper
Full-text available
The most daunting and challenging task in intrusion detection is to distinguishing between normal and malicious traffics effectively. In order to complete such a task, the biological danger theory has appeared to be one of the most appealing immunological models which has been converted to a computer science algorithm, named as Dendritic Cell Algor...
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A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi–Sugeno–Kang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation app...
Article
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The k nearest-neighbour (kNN) algorithm has enjoyed much attention since its inception as an intuitive and effective classification method. Many further developments of kNN have been reported such as those integrated with fuzzy sets, rough sets, and evolutionary computation. In particular, the fuzzy and rough modifications of kNN have shown signifi...
Article
Full-text available
Egocentric action recognition has been intensively studied in the fields of computer vision and clinical science with applications in pervasive health-care. The majority of the existing egocentric action recognition techniques utilize the features extracted from either the entire contents or the regions of interest in video frames as the inputs of...
Conference Paper
Full-text available
Formal concept analysis is a powerful tool in analyzing data and extracting rules from the formal context. The main framework of formal concept analysis is concept lattice which essentially describes the relationship between the objects and the attributes. And each node of the concept lattice is a formal concept. When processing the uncertain infor...
Conference Paper
The Quality of Services (QoS) is the measure of data transmission quality and service availability of a network, aiming to maintain the data, especially delay-sensitive data such as VoIP, to be transmitted over the network with the required quality. Major network device manufacturers have each developed their own smart dynamic QoS solutions, such a...
Article
The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally, through a competitive learning process, the SOMO algorithm searches for the minimum of an objective function. The MaxMin-SOMO algor...
Chapter
Full-text available
The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usua...
Article
Full-text available
Due to the significant efficiency and simple implementation, extreme learning machine (ELM) algorithms enjoy much attention in regression and classification applications recently. Many efforts have been paid to enhance the performance of ELM from both methodology (ELM training strategies) and structure (incremental or pruned ELMs) perspectives. In...
Chapter
Full-text available
The demand for multi-label classification methods continues to grow in many modern applications, such as document classification, music categorisation, and semantic scene classification. This paper proposes two multi-label fuzzy similarity-based nearest-neighbour algorithms using the association rules. Specifically, in order to reduce the combinati...
Article
Full-text available
Many strategies have been exploited for the task of reinforcing the effectiveness and efficiency of extreme learning machine (ELM), from both methodology and structure perspectives. By activating all the hidden nodes with different degrees, local coupled extreme learning machine (LC-ELM) is capable of decoupling the link architecture between the in...
Article
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Mammographic risk analysis is an important task for assessing the likelihood of a woman developing breast cancer. It has attracted much attention in recent years as it can be used as an early risk indicator when screening patients. In this paper, a kernel-based fuzzy-rough nearest-neighbour approach to classification is employed to address the issu...
Article
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The self-organizing map (SOM) approach has been used to perform cognitive and biologically inspired computing in a growing range of cross-disciplinary fields. Recently, the SOM based neural network framework was adapted to solve continuous derivative-free optimization problems through the development of a novel algorithm, termed SOM-based optimizat...
Conference Paper
Full-text available
The Semantic Web is an extension of the current World Wide Web, and aims to help computers to understand and process web information automatically. In recent years, the integration ontologies and rules has become a central topic in the Semantic Web. Therefore, significant research efforts have focused on integration description logic programs. Howe...
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Fuzzy-rough sets have enjoyed much attention in recent years as an effective way in which to extend rough set theory such that it can deal with real-valued data. More recently, fuzzy-rough sets have been employed for the task of classification. This has led to the development of approaches such as fuzzy-rough nearest-neighbour (FRNN) and its extens...
Article
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Local coupled feedforward neural networks (LCFNNs) help address the problems of slow convergence and large computation consumption caused by multi-layer perceptrons structurally. This paper presents a modified gradient-based learning algorithm in an attempt to further enhance the capabilities of LCFNNs. Using this approach, an LCFNN can achieve qua...
Article
Full-text available
1 Mammographic risk analysis is an important and challenging issue in modern medical science; re-search and development in this area has recently at-tracted much attention. Many efforts have been de-voted to achieving a higher accuracy in such analysis. This paper presents a novel approach for automated analysis of mammographic risk, in support of...
Conference Paper
Full-text available
Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical viewpoints. These problems have motivated the hybridisation of fuzzy-rough sets with kernel methods. Existing work which hyb...
Conference Paper
Full-text available
The assessment of mammographie risk analysis is an important issue in the medical field. Various approaches have been applied in order to achieve a higher accuracy in such analysis. In this paper, an approach known as Extreme Learning Machines (ELM), is employed to generate a single hidden layer neural network based classifier for estimating mammog...

Projects

Projects (2)
Project
The published works for video action recognition during my PhD period.