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Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification...
Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support...
In this paper, we hybridize the improved gravitational search algorithm (IGSA) with kernel based extreme learning machine (KELM) method. Based on this, a novel hybrid system IGSA-KELM is proposed to improve the generalization performance for classification problems. In this system, IGSA is designed by combining the search strategy of particle swarm optimization and GSA to effectively reduce the problem...
The core objective of this paper is to improve the performance of Content Based Image Retrieval (CBIR) system for biological image by intelligent selection of discriminative feature sets from the set of canonical features. The performance of the CBIR system can be further enhanced by proper selection of Classifier and fine tuning model parameters to obtain improved classification accuracy. We extracted...
This paper presents a novel combination of filter features selection algorithms for classification problem. Feature selection is one of the most important issues in pattern recognition, machine learning and computer vision. The main objective of feature selection regards the dimensionality reduction, the performance of machine learning improvement and the process comprehensibility increase. Exhaustive...
Imperfection of remote sensing data greatly affects the performance of information fusion algorithm. To solve this problem, a Gaussian kernel-based Fuzzy Rough Set fusion algorithm is proposed, since Fuzzy Rough Set theory is an effect tool to model uncertainties of data. For feature reduction a novel index is proposed to evaluate the significance of features, considering both the relevance between...
Feature selection techniques become significant part of many bioinformatics and biomedical applications. Choosing the important features is essential for biomarker discovery, provide better understanding of the data and potentially improve prediction performance. However, as the number of samples in dataset is small, the feature selection tends to be unstable. In this paper, the stability of five...
Aim at the problems of low classification accuracy rate of the traditional single feature and the multi-features dimension disaster, a ensemble learning algorithm based on multi-features fusion and selection is proposed, and is used for polarimetric SAR image classification. Firstly, various features of SAR image is extracted and fused by normalized; then, different feature selection methods are used...
In original data, there may exist redundant features, irrelevant features, noisy features besides informative features. Extracting informative features while eliminating the others is the goal of feature selection. This paper proposed a new feature selection algorithm based on Relief algorithm and SVM-RFE algorithm, and it is strongly targeted to eliminate the unnecessary features. Finally, We test...
The aim of this paper is to propose a new method of solving feature selection problem. Foundations of presented algorithm lie in the theory of rough sets. Feature selection methods based on rough sets have been used with success in many data mining problems, but their weakness is their computational complexity. In order to overcome the above-mentioned problem, researches used diverse approximation...
The Polish State Fire Service gathers information about incidents which require their intervention. This information is stored to document the events. However, it can be very useful for new officers training, better identification of threats and planning of more effective procedures. The identification of key risk factors for casualties among firefighters, children or other involved people was a topic...
Several methods for object category recognition in RGB-D images have been reported in literature. These methods are typically tested under the same conditions (which we can consider a “domain” in a restricted sense) such as viewing angles, distances to the object as well as lightening conditions on which they are trained. However, in practical applications one often has to deal with previously unseen...
High accuracy pedestrian detection plays an important role in all intelligent vehicles. This paper describes a system for detecting the obstacles in front of the vehicle and classifying them in pedestrians and non-pedestrians. It acquires the traffic scenes using a low-cost pair of gray intensities stereo cameras. A SORT-SGM stereo-reconstruction technique is used in order to obtain high density and...
Numerous methods based on the content based filtering is available for email spam identification. Dimensionality of the feature space is recognized as one of the leading factors that affect the efficiency in classifying mails. This study identifies feature selection techniques used in the general text classification for spam filtering. Also, the classification and prediction is performed using different...
Classification of emotion from sentences requires the classifier to be trained on relevant features. This paper focuses on different features (a) Bag-of-Words (b) Part-of-Speech tags (c) Sentence Length and (d) Lexical Emotion Features. Extensive evaluation on variable feature length for classifying textual emotions is carried out to understand their role in model performance. Experiments depict that...
In this paper we present a physical structure detection method for historical handwritten document images. We considered layout analysis as a pixel labeling problem. By classifying each pixel as either periphery, background, text block, or decoration, we achieve high quality segmentation without any assumption of specific topologies and shapes. Various color and texture features such as color variance,...
In order to increase the accuracy of abnormal event detection in crowd video surveillance, this paper proposes a novel hybrid optimization of feature selection and support vector machine (SVM) training model based on genetic algorithm. For reducing dimensions of multi-feature, we propose an adaptive genetic simulated annealing algorithm (ASAGA) feature selection method. The ASAGA takes advantage of...
Features used for classification play essential role in the performance of system. In the field of Lip reading, features appear in large number which has to be solved by selection of subset of features. Work covered in this paper validates the performance of individual visual features such as lip height, lip width, area of lip region, angles at corners and then combine them to create a new subset...
Linux is the most renowned open source operating system. In recent years, the number of malware targeting Linux OS has been increased and the traditional defence mechanisms seems to be futile. We propose a novel non-parametric statistical approach using machine learning techniques for identifying previously unknown malicious Executable Linkable Files (ELF). The system calls employed as features extracted...
In this article, a non-signature based statistical scanner for metamorphic malware detection, employing feature ranking methods like Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF), Galavotti-Sebastiani-Simi Coefficient (GSS), Term Significance (TS) and Odds Ratio (OR) is proposed. Malware and benign models for classification are created by considering top ranked features obtained...
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