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Spam E-mailis a kind of electronic spam in which unsolicited messages are sent by E-mail. It is themost severe problem world-wide for decades. One of the best approach to identify spam E-mails is filtering E-mails by classification. In many applications feature selection isthe most widely used and essential task in many classification techniques to reduce the dimensionality of feature space. In this...
Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built...
Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification...
Network Intrusion Detection System (NIDS) plays an important role in providing network security. Efficient NIDS can be developed by defining a proper rule set for classifying network audit data into normal or attack patterns. Generally, each dataset is characterized by a large set of features, but not all features will be relevant or fully contribute identifying an attack. Since different attacks...
In this paper, a Generalized Kernel-based Ensemble Learning (GKEL) algorithm for hyperspectral classification problems is presented. The proposed algorithm generalizes the Sparse Kernel-based Ensemble Learning (SKEL) technique, developed previously by the authors. SKEL optimally and sparsely weights and aggregates an ensemble of individual SVM classifiers which independently conduct learning within...
This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination...
The source data of intrusion detection system (IDS) are characteristic of heavy-flow, high-dimension and nonlinearity. A frequent problem in IDS is the choice of the right features that give rise to compact and concise representations of the network data; the other is how to improve the detection efficiency and accuracy of IDS under the small sample conditions. In order to delete the redundant and...
In daily interactions, humans convey their emotions through facial expression and other means. There are several facial expressions that reflect distinctive psychological activities such as happiness, surprise or anger. Accurate recognition of these activities via facial image analysis will play a vital role in natural human-computer interfaces, robotics and mimetic games. This paper focuses on the...
Support Vector Machines (SVM) for image classification proved to perform well in many applications. However, they are often not preferred in hyperspectral image analysis due to long processing times caused by a high number of support vectors and large data sets. We present two approaches that speed-up the classification process with SVM by a) simplifying the original SVM, i.e. reducing the number...
Feature selection is a very important part for datamining, machinery learning and pattern recognition. Distance plays a vital role in Support Vector Machines (SVM) theory. Relief-F algorithm solves feature redundancy well but doesn't guarantee the maximum distance. To overcome this problem, a feature subset selection algorithm is proposed which takes SVM average distance as estimation rule and sequential...
Radar high resolution range profile (HRRP) is sensitive to the target aspect and highly overlapped in feature space between different targets, thereby hybrid features are suitable for representing the target's property. In this paper, the quadratic spline wavelet with compact support properties was used to extract the energy spectrum features of HRRP by multi-resolution decomposition, and the power...
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...
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...
In this paper, features of steel defects data are selected using a wrapper algorithm to increase classification performance. The data are constructed using images of steel defects which are classified two classes as defects and pseudo defects. The suggested algorithm selects features which are relevant to class using the kappa statistic. This measure is suggested to improve accuracy of minor class...
This work focuses on the recognition of three-dimensional colon polyps captured by an active stereo vision sensor. The detection algorithm consists of SVM classifier trained on robust feature descriptors. The study is related to Cyclope, this prototype sensor allows real time 3D object reconstruction and continues to be optimized technically to improve its classification task by differentiation between...
Automatic prediction of protein three-dimensional structures from its amino acid sequence has become one of the most important researched fields in bioinformatics. With that increases the importance of determining the quality of these protein models. Protein three-dimensional structure evaluation is a complex problem in computational structure biology. We attempt to solve this problem using SVM and...
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 currently proposed Multi-surface Proximal Support Vector Machine Classification via Generalized Eigenvalues (GEPSVM) is an effective method on 2-class problem, which only needs to proximally solve two not parallel planes corresponding to each of two data sets, and the planes can be easily obtained by solving generalized eigenvalues. However, this approach can not effectively constrain the effect...
Multiple kernel learning (MKL) approach for selecting and combining different representations of a data is presented. Selection of features from a representation of data using the MKL approach is also addressed. A base kernel function is used for each representation as well as for each feature from a representation. A new kernel is obtained as a linear combination of base kernels, weighted according...
Deception is an everyday occurrence across all communication media. Deception detection on Chinese text is vital to the safety of people's life, the survival of enterprises and the stability of the country. Inundated with massive amounts of textual information transmitted through computer-mediated communication (CMC), people remain largely unsuccessful and inefficient in detecting those messages that...
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