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.
Two algorithms for building classification trees, based on Tsallis and Rényi entropy, are proposed and applied to customer churn problem. The dataset for modeling represents highly unbalanced proportion of two classes, which is often found in real world applications, and may cause negative effects on classification performance of the algorithms. The quality measures for obtained trees are compared...
E-learning has become an essential factor in the modern educational system. In today's diverse student population, E-learning must recognize the differences in student personalities to make the learning process more personalized. The objective of this study is to create a data model to identify both the student personality type and the dominant preference based on the Myers-Briggs Type Indicator (MBTI)...
Multi-label learning is the term used to express a type of supervised learning that requires classification algorithms to learn from a set of examples; each example can belong to one or multiple labels. The learning task consists of breaking the multi-label classification problem into several single label classification problems. This learning process results in the prediction of new class labels...
The Zernike moments can achieve high accuracy and strong robustness for the classification and retrieval of images, but involve huge amount of computation caused by its complex definition. It has limited its exploitation in online real-time applications or big data processing. So researches on how to improve the computation speed of Zernike moments are carried out. One of the existing high-accuracy...
Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
Network traffic classification plays an extremely important role in network management and service. Support vector machine (SVM) is widely adopted to classify traffic flows for its high accuracy. All features selected are treated equally in traditional SVM network traffic classification, which take little consideration of that each feature exerts a different influence on classification. Therefore,...
Ensemble techniques have been widely used for improving the classification performance, and recent studies show that ensembling classifiers through multi-modal perturbation can further improve the classification performance. In this paper, we propose a selective ensemble algorithm based on multi-modal perturbation (called SE_MP). In SE_MP, we devise a multi-modal perturbation method based on sampling...
In the last years, numerous investigations have been made within the field of faults diagnosis in induction motors. Most of them use data obtained either from the time domain, through advanced techniques in the frequency domain or even by simulation tools. Some researchers have employed a considerable effort in designing sophisticated algorithms to achieve the best performance of the diagnosis system...
Feature selection algorithm has a great influence on the accuracy of text categorization. The traditional information gain (IG) feature selection algorithm usually selects the features that rarely appear in the specified categories, but frequently appear in other categories. To overcome this drawback, on the basis of in-depth analysis of the related algorithms, an improved IG feature selection method...
Text classification is one of the key methods used in text mining. Generally, traditional classification algorithms from machine learning field are used in text classification. These algorithms are primarily designed for structured data. In this paper, we propose a new classifier for textual data, called Supervised Meaning Classifier (SMC). The new SMC classifier uses meaning measure, which is based...
Depth of anesthesia is a matter of great importance in surgery. Determination of depth of anesthesia is a time consuming and difficult task carried out by experts. This study aims to decide a method that can classify EEG data automatically with a high accuracy and, so will help the experts for determination process. This study consists of three stages: feature extraction of EEG signals, feature selection,...
Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature,...
Traditional Support Vector Regression (SVR) Machine acts as approximating a regression function. This paper, however, proposes a novel multi-class classification approach based on the SVR framework, called Support Vector Regression Machine with Consistency (SVRC). The contributions of this paper are: (1) To implement multi-class classification task, were place the margin term with its l1 norm in the...
Feature selection is a strategy that aims at making text classifiers more efficient and accurate. In this paper, we proposed a novel feature selection method based on Tibetan grammar for Tibetan classification. Tibetan language express grammatical meaning through the function words and word order, and the function word has large proportions. By analyzing the Tibetan grammar and distribution of part...
In order to improve the issue about the low localization accuracy of unknown nodes, this paper proposes a kind of improved region segmentation localization algorithm. Under the precondition of building the mathematical model of region segmentation, the algorithm utilizes the ratio which the unknown node received signal strength from the beacon nodes comparing with the signal strength scale factor...
On the problem of covert channel detection, the traditional detection algorithms exist specific covert channel blind area, or it is useful for some kind of covert channel detection but ignore other covert channels. In order to solve this problem, in this paper proposes network covert channel analysis method based on the density multilevel two segment clustering. Firstly, the problem of covert channel...
In real environment, the protocol distribution of Network traffic is imbalance, and the generalization ability of supervised learning algorithm such as algorithm to C4.5 is poor. In order to improve the classification accuracy and stability of network traffic, a network traffic classification method based on Rotation Forest was proposed. In the method, PCA was used for feature reduction and C4.5 algorithm...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
This paper introduces a novel sequential approach to user movement analysis and tracking for indoor positioning systems. The algorithm utilizes stored reference and measured received signal strength indication (RSSI) data to determine the most likely movement paths represented as sequences of rectangular zones. It is demonstrated that the application of the proposed approach results in an improvement...
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.