The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been...
In this paper, we propose a real-time detection algorithm using a MCT AdaBoost classifier which detects two-wheeler in a blind spot. The proposed algorithm uses a cascade classifier generated by AdaBoost learning based on the MCT feature vector. The MCT AdaBoost classifier is composed of weak classifiers as many as the number of pixels of the detection window, and each pixel becomes a weak classifier...
One of the major causes of death in the world is Heart Failure. This disease affects directly the heart's pumping job. Because of this perturbation, nutriments and oxygen are not well circulated and distributed. The New York Heart Association has classified this disease into four different classes based on patient symptoms. In this paper, we are using a data mining technique, more precisely a sequential...
Numbers of samples in different classes are in nature imbalanced in many machine learning problems. Single classifier-based methods are subject to high variance. Therefore, ensemble-based methods are more suitable for dealing with imbalanced pattern classification problems. In this work, we propose a boosting-based method: BSMBoost which creates an ensemble of classifiers using samples selected by...
With the development of the aviation industry and the improvement of people's living standard, more and more people choose aircraft as their way of travel, but the airline adjusts the price according to the revenue management in real time. The purpose of this paper is to design different decision-making tools from the customer's perspective, and to provide customers with the relevant information needed...
The fuzzy restricted Boltzmann machine (FRBM) is demonstrated to have better generative and discriminative capabilities than traditional RBM. We now further investigate and compare the generative ability of DBN with FRBM on image reconstruction. The DBN is pre-trained by stacking RBMs layer by layer and then fine-tuned by the wake-sleep algorithm. Then the FRBM, RBM and DBN are compared in detail...
In this paper, a novel classifier for classification problems, based on increment support vector data description, is proposed. The proposed method is the expand version of increment support vector data description by bring in two classes of sample. Because of the addition of two kinds of sample. This method can reflect the target sample distribution state more complete in super ball space. The results...
Stacked auto-encoder is mainly used for image classification and it can extract valid information from data through unsupervised pre-training and supervised fine-tuning. This paper is intended to improve the accuracy of image classification, we constructed a 6-layer stacked convolution neural network (CNN) based on stacked auto-encoders. The constructed CNN can extract effective features for image...
In this paper, we propose to determine whether the viewer's behavior changes or not before, during and after watching a TV program. Are there any behaviors specific to each particular phase of viewing? Here, we propose a flexible and nonintrusive method based on the use of three categories of everyday connected objects (i.e. Smartphone, smartwatch and remote control). Data were collected during participants'...
This paper proposes an improved KNN algorithm to overcome the class overlapping problem when the class distribution is skewed. Different from the conventional KNN algorithm, it not only finds out the k nearest neighbors of each sample (even the test object itself) in the training dataset, but also the neighbors of the unknown test object. Then the validity value of a data point is computed based on...
This paper deals with classification algorithms as one of the basic principles of pattern recognition. We analyze their effect to a feature space and compare the type and the shape of the separating and decision surface, respectively. We proposed a novel classification approach based on Cumulative Fuzzy Membership Function that creates a decision surface in a different way as an MF ARTMAP neural network...
Research on activity recognition provides a wide range of ubiquitous computing applications. Once activities are recognized, computers can use this information to provide people with suitable services. In the past decade, many classification algorithms have been applied to activity recognition. However, most of them were based on the use of inertial measurement sensors, such as tri-axial accelerometers...
Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient...
In order to solve the problems such as availability of data extraction, better local optimum, gradient to dissipate more efficiently, this paper presents a new method of power transformer fault diagnosis based on deep learning and Softmax classifier. Power transformer fault diagnosis model is established based on stacked auto-encoders and softmax regression, then each restricted boltzmann machine...
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...
At present, most of the EEG emotion recognition studies have taken all electric shocks or filtered electrodes as a feature and they are integrated (combined) with simple features that are extracted from other signals as a single classifier Emotional classification, but there are problems such as low efficiency and low accuracy. Aiming at this problem, this paper proposes an EEG emotion classification...
Advances in highly multi-parametric measurements by mass cytometry have made possible the accurate detection of acute myeloid leukemia (AML) cells in complex cell populations. However, current informatics methods bottlenecks data processing by being labor-intensive, time-consuming, and prone to user bias. To address these problems, major efforts have been made to automate the detection of AML cells...
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...
High quality teaching has always been the pursuit of universities, but the automatic and real time evaluation on the quality of classroom teaching has not been achieved. To solve this problem, a real-time processing of classroom video through face detection technology and a software system to provide a basis for judging the quality of teaching is proposed in this paper. The main application of machine...
The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.