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DDoS attacks bring huge threaten to network, how to effectively detect DDoS is a hot topic of information security. Currently, there are some methods designed to detect DDoS attacks, but the detection rate of them is low. Moreover, DDoS detection is easily misled by flash crowd traffic. In this paper, a new method to detect DDoS attacks based on RDF-SVM algorithm is proposed. By considering the importance...
Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient...
Herlev dataset consists of 7 cervical cell classes, i.e. superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ is considered. The dataset will be tested to classify two classes, consisting of normal and abnormal cells. Seven different cell types will be classified to separate the cells into 7 classes which are 3 normal cell...
At present, the classification of brain diseases through neuroimaging data is a hot topic. Attention deficit hyperactivity disorder (ADHD) is usually diagnosed by the standard scale. However, the traditional diagnostic methods have high misdiagnosis rate and time consuming. In this paper, we discussed the classification of ADHD by using the feature subset obtained by preprocessing and feature selection...
the division of the test paper can reflect the quality of examination paper, but it is difficult to find some decisive courses in dozens of courses. In order to find out the curriculum that decides the role of different levels of students, the concept of course discrimination is proposed, which focuses on the value of course discrimination, the classification method and the proportion of special courses...
Exponential discriminant analysis (EDA), which solves the singularity problem of the conventional linear discriminant analysis (LDA) using diffusion mapping, has been wildly used in dealing with supervised classification and dimensionality reduction problems for its simplicity and robustness. However, the discriminant rules of the EDA algorithm involve a linear combination of all features, thus may...
Feature selection has become a necessary step to the analysis of high-dimensional datasets coming from several application domains (e.g., web data, document and image analysis, biological data). Though well-established methods exist to select highly discriminative features, discarding the ones that may be either redundant or irrelevant to the problem at hand, little attention has been so far given...
Feature importance is the process where the individual elements of a machine learning model's feature vector are ranked on their relative importance to the accuracy of that model. Some feature ranking algorithms are specific to a single model type, such as Garson and Goh's neural network weight-based feature ranking algorithm. Other feature ranking algorithms are model agnostic, such as Brieman's...
Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering… etc.). This paper proposes an efficient approach to be used in reducing the size of the feature vector for web text document classification process. This approach is based on using...
Feature selection is an important tool used in data reduction; it aims at improving efficiency in many machine-learning algorithms by choosing a small set of informative features among the whole dataset. Feature selection algorithms can be classified in three major categories: Filter, Wrapper and Embedded. In this paper, we proposed a new hybrid filter-wrapper algorithm of feature selection based...
Large amounts of data gets accumulated and stored in the databases in day to day life that are high dimensional in nature. The data mining task is used to excavate the useful information from the high dimensional data. To classify or cluster the high dimensional data, the dimensionality of the data needs to be reduced. Feature selection is used to select the features that are relevant to the analysis...
Feature selection process involves identifying a subset of features that provides same results as the original entire set of features. Feature subset selection removes irrelevant and redundant features for reducing data dimensionality. Feature selection, also known as attribute subset selection. A feature selection algorithm can be measured from both the efficiency and effectiveness points. The efficiency...
The scale of big data is increasing in every minute, and it becomes important to handle massive data. The familiar problem of Big data is not only huge volume but also planned in many places to provide high dimensionality in feature selection. In numerous big data application, feature selection is significant to select the essential features from the known data set and it removes unrelated and disused...
Feature selection is an essential method in which we identify a subset of most useful ones from the original set of features. On comparing results with original set and identified subset, we observe that the results are compatible. The feature selection algorithm is evaluated based on the components of efficiency and effectiveness, where the time required and the optimality of the subset of the feature...
To improve the accuracy and efficiency of facial expression classification, a facial expression recognition method using Gabor features and Adaboost classifiers is proposed. Local regions that best represent facial expressions are first segmented and located, and then the Gabor features of the local regions are extracted. Gabor features are selected by using distance discrimination and feature ranking,...
Big data analytics is emerging as an important research field nowadays with many technical challenges that confront both commercial IT deployment and big data research communities. One of the inherent problems of big data is the curse of dimensionality. Modern data are described with many attributes and stored with high dimensions. In data analytics, feature selection has been popularly used to lighten...
Feature selection is a very important technique in machine learning and pattern classification. Feature selection studies using batch learning methods are inefficient when handling big data in real world, especially when data arrives sequentially. Online Feature Selection is a new paradigm which is more efficient than batch feature selection methods but it still very challenging in large-scale ultra-high...
In text classification, feature selection is essential to improve the classification effectiveness. This paper provides an empirical study of a feature selection method based on genetic algorithms for different text representation methods. This feature selection algorithm can accomplish two goals: in one hand is the search of a feature subset such that the performance of classifier is best; in other...
The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm,...
Gene selection is an important pre-processing step in microarray analysis and classification. While traditional gene selection algorithms focus on identifying relevant and irredundant genes, we present a new gene selection algorithm that chooses gene subsets based on their interaction information. Many individual genes may be irrelevant with the class, but when combined together, they can interact...
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