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.
The technology of information filtering may help the people to pick out the interested information and shield the unnecessary information. Facing the new challenge of the real-time online network information filtration, the technology of the adaptive information filtering appears to be very important in this case. In aspects of the self-learning of user template for adaptive information filtering,...
Iris recognition is a kind of novel biometric feature recognition approach which was developed from 1990s and it has attracted more and more attention because of its high accuracy. In this paper, based on researching the existing iris authentication methods, a novel iris feature selection approach based on practical swarm optimization is proposed. We use the improved wavelet modulus maximum to locate...
We propose a Memetic algorithm for feature selection in volumetric data containing spatially distributed clusters of informative features, typically encountered in neuroscience applications. The proposed method complements a conventional genetic algorithm with a local search utilizing inherent spatial relationships to efficiently identify informative feature clusters across multiple regions of the...
In the Network Intrusion Detection, the large number of features increases the time and space cost, besides the irrelative redundant characteristics make the detection accuracy dropped. In order to improve detection accuracy and efficiency, a new Feature Selection method based on Rough Sets and improved Genetic Algorithms is proposed for Network Intrusion Detection. Firstly, the features are filtered...
Given an image from a biometric sensor, it is important for the feature extraction module to extract an original set of features that can be used for identity recognition. This form of feature extraction has been referred to as Type I feature extraction. For some biometric systems, Type I feature extraction is used exclusively. However, a second form of feature extraction does exist and is concerned...
Dissimilarities can be a powerful way to represent objects like strings, graphs and images for which it is difficult to find good features. The resulting dissimilarity space may be used to train any classifier appropriate for feature spaces. There is, however, a strong need for dimension reduction. Straightforward procedures for prototype selection as well as feature selection have been used for this...
This paper presents a genetic programming (GP) based approach for designing classifiers with feature selection using a modified crossover operator. The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. To overcome the difficulties with standard crossover operator,...
One of the most important modules of any bio-metric system is the feature extraction module. Given a sample it is important for the feature extraction method to extract a rich set of features that can be used for identity recognition. This form of feature extraction has been referred to as Type I feature extraction and for some biometric systems it is used exclusively. However, a second form of feature...
This work explores the use of clustering objectives in a Multi-Objective Genetic Algorithm (MOGA) for both, feature selection and cluster count optimization, under the application of flow based encrypted traffic identification. We first explore whether it is possible to achieve the performance of a gold standard model (i.e., classification objectives), using a MOGA based on clustering objectives....
Surrogate models of fitness have been presented as a way of reducing the number of fitness evaluations required by an evolutionary algorithm. This is of particular interest with expensive fitness functions where the cost of building the model is outweighed by the saving of using fewer function evaluations. In this paper we show how a Markov network model can be used as a surrogate fitness function...
In the past, we proposed a GA-based clustering method for attribute clustering and feature selection. The fitness of each individual was evaluated using both the average accuracy of attribute substitutions in clusters and the cluster balance. The evaluation was, however, quite time-consuming. In this paper, we modify the previous method for a better execution performance based on feature similarity...
Many search strategies have been exploited in implementing feature selection, in an effort to identify smaller and better subsets. Such work typically involves the use of heuristics in one form or another. In this paper two novel methods are presented by applying harmony search to feature selection. In particular, it demonstrates the potential of utilising this search mechanism in combination with...
Aiming at the problem of how to determine the dimensions of the eigenvectors in principal component analysis (PCA), this paper presents a novel feature selection method based on improved chaos genetic algorithm (ICGA). First, two kinds of chaotic mappings are introduced in different phase of ICGA, which maintain the diversity of population and enhance the global searching capability; Second, this...
The cancer classification through gene expression patterns becomes one of the most promising applications of the microarray technology. It is also a significant procedure in bioinformatics. In this study a grid computing based evolutionary mining approach is proposed as discriminant function for gene selection and tumor classification. The proposed approach is based on the grid computing infrastructure...
Automatic classification of plant diseases at an early stage is vital for precision crop protection. Our aim was to identify sugar beet leaves inoculated with Cercospora beticola before symptoms are visible. Therefore hyperspectral reflection between 400 and 1050 nm was observed. Relevant wavelengths have to be found in order to implement practical sensor systems with reduced development costs. The...
In this paper we propose a new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images, using Wavelets Transform (WT) as input to Genetic Algorithm (GA) and Support Vector Machine (SVM). The proposed method segregates MR brain images into normal and abnormal. Our contribution employs genetic algorithm for feature selection witch requires much lighter computational...
An accurate demand forecasting model has both academic and practical significance to supply chain management for China's retail industry. In this paper, we proposed a novel demand forecasting model named SHEnSVM (Selective and Heterogeneous Ensemble of Support Vector Machines), in which the individual SVMs are trained by different samples generated by bootstrap algorithm and different parameters generated...
Supervised classification in remote sensing imagery is receiving increasing attention in current research. In order to improve the classification ability, a lot of spatial-features (e.g., texture information generated by GLCM) have been utilized. Unfortunately, too many spatial-features often cause classifier over-fit to a certain features' character and lead to lower classification accuracy. The...
A major problem with text classification problems is the high dimensionality of the feature space. This paper investigates how genetic algorithm and k-means algorithm can help select relevant features in text classification. which uses the genetic algorithm (GA) optimization features to implement global searching, and uses k-means algorithm to selection operation to control the scope of the search,...
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.