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Classification of Alzheimer 's disease (AD) from normal control (NC) is important for disease abnormality identification and intervention. The current study focused on distinguishing AD from NC based on the multi-feature kernel supervised within- class-similarity discriminative dictionary learning algorithm (MKSCDDL) we introduced previously, which has been derived outperformance in face recognition...
In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection...
This paper aims to develop a framework for vehicle type classification using convolutional neural network based on vehicle rear view images. Compared with the extraction of the appearance features from vehicle side view and frontal view images, there has been relatively little research on vehicle type classification by using vehicle rear view images' information. The vehicle rear view images are detected...
Twitter is a popular microblogging service that allows its users to view and share limited character messages (known as “tweets”) with the public. This paper proposes a tweet sentiment classification framework which pre-processes information from Emoticon and Emoji in such way that their textual representation is included to enrich the tweet. Once the tweets are pre-processed, a hybrid computational...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
In this paper, we presented an improved vehicle detection algorithm based on object proposals. In the training part, by using Selective Search algorithm, we firstly segment the vehicle areas in the sample set as positive examples, other regions as negative examples. Then PHOG (Pyramid Histogram of Oriented Gradient) features of the positive samples and negative ones after separately being labeled...
When we use binary tree support vector machine (SVM) to work the multi-classification problems out, we always find that the structure of the binary tree has a large chance and it has a great influence on the classification efficiency of the classification model. To solve this problem, according to the idea of separating the most widely distributed class first, an improved binary tree SVM multiple...
Ventricular tachycardia, ventricular flutter, and ventricular fibrillation are malignant forms of cardiac arrhythmias, whose occurrence may be a life-threatening event. Several methods exist for detecting these arrhythmias in the electrocardiogram. However, the use of Gaussian process classifiers in this context has not been reported in the current literature. In comparison to the popular support...
In the paper, a rough spatial kernelized fuzzy c-means clustering (RSKFCM) based medical image segmentation algorithm is proposed. This technique is a combination of rough set and spatial kernelized fuzzy c-means clustering (SKFCM). SKFCM is failed to remove the indistinct knowledge that is associated with each data set during the process of its assignment to a particular cluster. The rough set is...
Fuzzy clustering has emerged as an important tool for discovering the structure of data. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering. Aimed at the problems of both a local optimum and depending on initialization strongly in the fuzzy c-means clustering algorithm (FCM), a method of kernel-based fuzzy c-means clustering based on fruit fly algorithms...
Based on the spectral data from SDSS, Kernel Support Vector Machines (K-SVM) is applied to classify quasars from other celestial body. Firstly, the basic theory of the SVM(Support Vector Machine) with relaxation factor and kernel function is introduced. Then, the main parameters are designed and selected. Finally, the method is applied to the classification and identification of the quasars. The classification...
Due to the particularity of the aero-engine bearing and the limitation of the test conditions, it is difficult to get enough fault class sample data and the misclassification cost that misjudge fault sample to normal sample is higher than the opposite misjudgment, therefore the diagnosis of aero-engine bearing belongs to the typical small sample problem which is also unbalance. In order to solve this...
Temporal sequences of images called Satellite Image Time Series (SITS) allow land cover monitoring and classification by affording a large amount of images. Many approaches attempt to exploit this multi-temporal data in order to extract relevant information such as classification-based techniques. In this paper we compare low and high levels classification-based approaches that aim to reveal the SITS...
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, highthroughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs among the candidates set remains timeconsuming and expensive. In addition, contrasting to the number of SNPs involved, the available datasets (samples) generally have fairly small sample...
Support Vector Machine (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed, accuracy, and/or scalability. The algorithm possesses parameters that need precision tuning to perform well. This work proposes a novel parallelized parameter...
With the advancement of data processing technology, it is a significance task for machine learning to handle massive amounts of data. The traditional classification method is a supervised learning method, which requires a large number of labeled samples. But it is difficult to achieve. In this paper, a semi-supervised learning algorithm combining co-training with support vector machine (SVM) classification...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
In the information age, sentiment classification of internet topics is of great significance. This paper proposes a microblog sentiment classification approach with parallel support vector machine (SVM). The proposed method integrates the features of microblog with preprocessing to ensure the data suitable for sentiment classification. After the preprocessing process, Apache Spark parallel SVM is...
Nowadays, there are a lot of graph data in many fields such as biology, medicine, social networks and so on. However, it is difficult to detect anomaly and get the useful information if we want to apply the traditional algorithms in graph data. Statistical pattern recognition and structural pattern recognition are two main methods in pattern recognition. The disadvantage of statistical pattern recognition...
The use of technology has grown extensively in many fields, including that of the medical sciences. However, there has not been a lot of research done in the field regarding the use of basic stance parameters to classify the presence or lack thereof of locomotive disorders. In this paper, we shall be presenting the most optimum classification algorithm for the binary classification of a variety of...
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