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, feature selection was carried out for multi-intelligence classification, and finds key regions. We designed different multi-intelligence tasks with BCI. SVM was used to classify and select features. The experiment reveals that a band has a greater effect on imagery intelligent tasks. And the introduced feature selection algorithm succeeded to detect key regions for multi-intelligence...
Unbalanced data, minority classes with few samples, present in many applications. It is difficult to solve the problems of unbalanced data by traditional methods. In this paper, a hybrid algorithm based on random over-sampling, decision tree (DT), particle swarm optimization (PSO) and feature selection is proposed to classify unbalanced data. The proposed algorithm has the ability to select beneficial...
Automatic document classification due to its various applications in data mining and information technology is one of the important topics in computer science. Classification plays a vital role in many information management and retrieval tasks. Document classification, also known as document categorization, is the process of assigning a document to one or more predefined category labels. Classification...
The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when...
A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration...
Gene expression data possess two main features: small samples and high dimensions. There are many difficulties on analyzing gene expression data using the traditional machine learning methods. In this paper we use an SVM-RFE based method to obtain the set of trait genes that are related to the disease-resistance property in rice and evaluate these genes according to some heuristics. And then we query...
The recently introduced Gini-Index Text (GIT) feature-selection algorithm for text classification, through incorporating an improved Gini Index for better feature-selection performance, has some drawbacks. Specifically, the algorithm, under real-world experimental conditions, concentrates feature values to one point and be inadequate for selecting representative features. As such, good representative...
Feature extraction for individual communication transmitter identification is one of the major issues in the identifying process. A neighborhood rough set is proposed in this paper, in order to search for the good feature subset. Then we present a SVM classification approach of weighted feature set based on the significance of an attribute. The result of experiments shows that the reduced feature...
This research constructs the CSO+SVM model for data classification through integrating cat swam optimization into SVM classifier. There are two factors (i.e. feature selection and parameter determination) of classification problems will mainly discuss in this study. The objectives of feature selection are to reduce number of features and remove irrelevant, noisy and redundant data. Besides, the parameter...
Intrusion detection is a critical component of secure information systems. Data Intrusion Detection Processing System often contains a lot of redundancy and noise features, bringing the system a large amount of computing resources, a long training time, a poor real-time, and a bad detection rate. For high dimensional data, feature selection can find the information-rich feature subset, thus enhance...
Recent researches have investigated the impact of feature selection methods on the performance of support vector machine (SVM) and claimed that no feature selection methods improve it in high dimension. However, they have based this argument on their experiments with simulated data. We have taken this claim as a research issue and investigated different feature selection methods on the real time micro...
Feature selection and feature weight calculating are key preprocesses in text classification. A new feature selection approach based on average interaction gain (AIG) is presented and a new feature weight adjustment technique (WA) taking inter-class distribution and intra-class distribution into consideration is presented too. Then a new approach combining AIG with WA called AIG-WA is presented. In...
With the rapid growth in the credit industry, credit scoring classifiers are being widely used for credit admission evaluation. Effective classifiers have been regarded as a critical topic, with the related departments striving to collect huge amounts of data to avoid making the wrong decision. Finding effective classifier is important because it will help people make an objective decision instead...
Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses...
This paper presents the clonal selection algorithm (CSA) to select a proper subset of features and optimal parameters of support vector machines (SVMs) classifier. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution to select better parameters, in our experiment, to improve classification accuracy, the clonal selection algorithm and genetic algorithm are used to reach...
This study proposes a new strategy combining with the SVM(support vector machine) classifier for features selection that retains sufficient information for classification purpose. Our proposed approach uses F-score models to optimize feature space by removing both irrelevant and redundant features. To improve classification accuracy, the parameters optimization of the penalty constant C and the bandwidth...
The processing of data from the database using data mining algorithms need more special methods. In fact, some redundancy and irrelevant attributes reduce the performance of data mining, so the problem of feature subset selection becomes important in data mining domain. This paper presents a new algorithm which is called discrete binary differential evolution (BDE) algorithm to select the best feature...
Chronic pain is a common long-term condition that changes patients' physical and emotional functioning. Currently, the integrated biopsychosoical approach is the mainstay treatment for patients with chronic pain. Self reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. Nevertheless, a large number of questions (for example 329 questions in this study)...
There is still a problem, lack of enough generalization ability, with existing feature selection methods. To solve this problem, a supervised feature selection method base on support vector machine is proposed in view of generalization ability of support vector machine for small sample set and ability of processing high-dimensional data of kernel function. The new method introduces the category-separability...
Network intrusion detection system (NIDS) uses all data features which contain irrelevant and redundant features. These features influence both the performance of the system and the types of attacks that NIDS detects. At the same time, they cause slow training and testing process, system resource consumption expensive as well as low true detection rate. Therefore, feature selection is an important...
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.