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In clinical practice, the magnetic resonance imaging (MRI) is a prevalent neuroimaging technique for Alzheimer's disease (AD) diagnosis. As a learning using privileged information (LUPI) algorithm, SVM+ has shown its effectiveness on the classification of brain disorders, with single-modal neuroimaging samples for testing but multimodal neuroimaging samples for training. In this work, we propose to...
Many software engineering problems are multi-objective in nature, which has been largely recognized by the Search-based Software Engineering (SBSE) community. In this regard, Pareto- based search algorithms, e.g., Non-dominated Sorting Genetic Algorithm II, have already shown good performance for solving multi-objective optimization problems. These algorithms produce Pareto fronts, where each Pareto...
The computer-aided histopathological image diagnosis has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm with a simple network architecture and parameters. In this work, we propose a random binary hashing (RBH) based PCANet (RBH-PCANet), which can generate multiple randomly encoded binary codes to provide more information. Moreover,...
In this paper we present a research on identification of audio recording devices from background noise, thus providing a method for forensics. The audio signal is the sum of speech signal and noise signal. Usually, people pay more attention to speech signal, because it carries the information to deliver. So a great amount of researches have been dedicated to getting higher Signal-Noise-Ratio (SNR)...
Classification of motor imagery (MI)-based electroencephalogram (EEG) signals is a key issue for the development of brain-computer interface (BCI) systems. The objective of this study is to develop an algorithm that can distinguish two categories of MI EEG signals. In this paper, we propose a new classification algorithm for two-class MI signals recognition in BCIs. The proposed scheme develops a...
A new method is proposed in this paper to detect emotions in music. Four audio features are used to classify emotions into six clusters with the RAKEL (Random klabelsets)multi-label classification. The Experiments show the rationality of proposed method and good performance on classification with the feature of beat spectrum.
Classification on noisy data streams has recently become one of the most important topics in streaming data mining. In this paper, a Classification algorithm for mining Data Streams based on Mixture Models of C4.5 and NB is proposed called CDSMM. In this algorithm, C4.5 is used as the base classifiers, the hypothesis testing method is introduced for the detection of concept drifts, and a Naïve Bayes...
Choquet integral with regards to a non-additive set function μ is a useful combination tool when we consider the interactions between classifiers. This combination method works very well at the expense of run time and the memory space. This paper introduces samples reduction technology to degrade the complexity of determining the non-additive set functions μ which is determined by genetic algorithm...
Email is a kind of semi-structured document, some important attributes are contained in its structure, and especially using spam-specific features could improve the email classification results. In this paper, we apply decision tree data mining technique to dig out the potential association rules among these attributes of email, and then to identify unknown email's category based on these rules. According...
This paper presents a PSO-based method for learning similarity measure of nominal features for case based reasoning classifiers (i.e. CBR classifiers). The symbolic features considered here takes completely unordered values. It has been indicated in that in specific classification task, the similarities between these nominal feature values can not be simply considered as either 0 or 1. A GA-based...
Supervised classification of fully polarimetric SAR image using neural network is a common method nowadays. As an effective learning method of neural network, BP algorithm is the most widespread one in the neural network algorithms. However, BP network is easy to fall into local extremum and exists shortcomings such as the slow training process. To this end, this paper presents a method of supervised...
In order to efficient, objective and comprehensive assessment of wheat flour processing accuracy, this paper introduces a new method to detect the wheat flour processing precision; it uses wheat flour three features of Whiteness, color, bran to design classifiers. The 240 different accuracy wheat sample images were analyzed and tested, experimental results show that CIE L*a*b*and OTSU algorithm can...
In this paper we studied privacy preserving distributed data mining. The existing methods focus on a universal approach that exerts preservation in the same degree for all persons, without catering for their concrete needs. In view of this we innovatively proposed a new framework combining the secure multiparty computation (SMC) with K-anonymity technology, and achieved personalized privacy preserving...
Classification algorithm is a kind of important technology in data mining, and the most commonly used is decision tree learning. In the process of constructing a decision tree, the selecting criteria of splitting attributes will directly affect the classification results. And the attribute selection of the traditional decision tree algorithm is based on information theory. In this paper, by combining...
An implementation of data preprocessing system for Web usage mining and the details of algorithm for path completion are presented. After user session identification, the missing pages in user access paths are appended by using the referer-based method which is an effective solution to the problems introduced by using proxy servers and local caching. The reference length of pages in complete path...
In this paper, a local watermarking scheme in the ridgelet domain combining image content and JND model is presented. Since the ridgelet transform (RT) can efficiently represent image with linear singularities and has directional sensitivity, the image is partitioned into small pieces. And these small pieces are classified into different characteristic categories (S1 with weak texture, S2 with strong...
Analysis by way of the experiment, compared with ID3 algorithm, there is a large difference between SD-CA algorithm in this thesis and decision tree algorithm originated from ID3 And the classification rule is also different from the practice. It relates to the data containing middling, the proportions are all 0.5. Then, its results to classification are much more related to other attributes; some...
The attribute reduction of information system can improve the accuracy of knowledge discovery, machine learning, etc. and it also can improve the efficiency. This paper proposes an attribute testing reduction algorithm, the algorithm can make the information system retain as few as attributes under the condition that maintains the original style, it can not only save much time for the later system...
Several routing and wavelength assignment algorithms have been developed to reserve dedicated connections for high-performance applications on optical networks. In this paper, we present an analytical and experimental evaluation of these algorithms. Our experiments indicate that the minimum-hop feasible path algorithm maximizes network utilization. We also present novel algorithms for deferred wavelength...
A new method to solve the convex hull problem in n-dimensional spaces is proposed in this paper. At each step, a new point is added into the convex hull if the point is judged to be out of the current convex hull by a linear programming model. For the linear separable classification problem, if an instance is regarded as a point of the instances space, the overlap does not still occur between the...
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