Medoids-based fuzzy relational clustering generates clusters of objects based on relational data, which records pairwise similarity or dissimilarities among objects. Compared with single-medoid based approaches, multi...
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Most of the microarray expression data have tens of thousands of genes but very small number of samples. Feature selection has been widely used to extract the subset of informative genes. Though many feature selection...
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The main purpose of this paper is to investigate the connection between the Painlev′e property and the integrability of polynomial dynamical systems. We show that if a polynomial dynamical system has Painlev′e prope...
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The main purpose of this paper is to investigate the connection between the Painlev′e property and the integrability of polynomial dynamical systems. We show that if a polynomial dynamical system has Painlev′e property, then it admits certain class of first integrals. We also present some relationships between the Painlev′e property and the structure of the differential Galois group of the corresponding variational equations along some complex integral curve.
This paper presents a biologically inspired local image descriptor that combines color and shape features. Compared with previous descriptors, red-cyan cells associated with L, M, and S cones （L for long, M for mediu...
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This paper presents a biologically inspired local image descriptor that combines color and shape features. Compared with previous descriptors, red-cyan cells associated with L, M, and S cones （L for long, M for medium, and S for short） are used to indicate one of the opponent color channels. Stepping forward from state-of-the-art color feature extraction, we exploit a new approach to compute the color orientation and magnitudes of three opponent color channels, namely, red-green, blue-yellow, and red-cyan, in two-dimensional space. Color orientation is calculated in histograms with magnitude weighting. We linearly concatenate the four-color-opponent-channel histogram and scale-invariant-feamre-transform histogram in the final step. We apply our biologically inspired descriptor to describe the local image feature. Quantitative comparisons with state-of-the-art descriptors demonstrate the significant advantages of maintaining invariance to photometric and geometric changes in image matching, particularly in cases, such as illumination variation and image blurring, where more color contrast information is observed.
In this paper, we investigate the problem of evaluating the performance of classification models. First of all we propose the concept of weighted correct pair map. Then based on the weighted correct pair map, we propo...
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In this paper, we investigate the problem of evaluating the performance of classification models. First of all we propose the concept of weighted correct pair map. Then based on the weighted correct pair map, we proposed a new evaluation measure. The attractive features of the measure are that it is insensitive to imbalanced class distributions and discriminating enough. Experimental results demonstrate that the proposed measure is reliable. The work presented in this paper may stimulate new research in classification model designing, such as designing new optimization-based classification or ranking models.
In this paper, we propose an iris recognition algorithm based on probabilistic features. At the image preprocessing step, we do many experiments on iris segmentation in order to minimize the negative influence on reco...
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In this paper, we propose an iris recognition algorithm based on probabilistic features. At the image preprocessing step, we do many experiments on iris segmentation in order to minimize the negative influence on recognition caused by eyelash and eyelid. As different segmentation angle has different results, we have determined an ideal angle for further recognition. At the recognition step, iris is transformed into frequency domain by using Log Gabor filters. Then raw features are extracted. Probabilistic features, with two forms of distribution, random distribution and normal distribution, are set of vectors generated from these raw features. At the matching step, the raw features are replaced by the property features. KNN algorithm is used to decide the final result. Experiment results showed that the proposed method can make further improvement of recognition rates and reduce the time consumption significantly.
For emerging vehicle ad hoc network, the security problem is the biggest problem facing at present. The network is vulnerable to hacking and attacks. Generally, to join a region of the vehicle Ad Hoc networks, a vehic...
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For emerging vehicle ad hoc network, the security problem is the biggest problem facing at present. The network is vulnerable to hacking and attacks. Generally, to join a region of the vehicle Ad Hoc networks, a vehicle node needs to be verified its identity by the roadside infrastructures first. This paper proposes an authentication mechanism based on threshold secret sharing mechanism without relying on the infrastructures. The proposed scheme conforms to the characteristics of the VANET, such as without the credible centre, self-organization and so on. The vehicle should be accepted by the other vehicles to communicate. The proposed authentication mechanism contains the generation of signing certificate, the update mechanism and the revocation mechanism. The availability and security of the network can be ensured in the scheme.
The rapid developments of chip-based technology have greatly improved human genetics and made routine the access of thousands of single nucleotide polymorphisms (SNPs) contributing to an informatics challenge. The cha...
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ISBN:
(纸本)9781450328104
The rapid developments of chip-based technology have greatly improved human genetics and made routine the access of thousands of single nucleotide polymorphisms (SNPs) contributing to an informatics challenge. The characterization and interpretation of genes and gene-gene interactions that affect the susceptibility of common, complex multifactorial diseases is a computational and statistical challenge in genome-wide association studies (GWAS). Various methods have been proposed, but they have dificulty to be directly applied to GWAS caused by excessive search space and intensive computational burden. In this paper, we propose an ant colony optimization (ACO) based algorithm by combining the pheromone updating rule with the heuristic information. We tested power performance of our algorithm by conducting suficient experiments including a wide range of simulated datasets experiments and a real genome-wide dataset experiment. Experimental results demonstrate that our algorithm is time efficient and gain good performance in the term of the power of prediction accuracy. Copyright 2014 ACM.
Density-based clustering over huge volumes of evolving data streams is critical for many modern applications ranging from network traffic monitoring to moving object management. In this work, we propose an efficient d...
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Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Geno...
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