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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...
The main aim of this work is to compare the performance of different algorithms for human activity recognition by extracting various statistical time domain and frequency domain features from the inertial sensor data. Our results show that Support Vector Machines with quadratic kernel classifier (accuracy: 93.5%) and Ensemble classifier with bagging and boosting (accuracy: 94.6%) outperforms other...
Opinion mining is an automation technique of textual data from opinion sentence that produce sentiment information. It is also called sentiment analysis that involves the construction of a system for collecting and classifying opinions about a product review done by understanding, extracting and processing the text in an opinion sentence become positive, negative, and neutral. One of the techniques...
Classification of text documents is commonly carried out using various models of bag-of-words that are generated using feature selection methods. In these models, selected features are used as input to well-known classifiers such as Support Vector Machines (SVM) and neural networks. In recent years, a technique called word embeddings has been developed for text mining and, deep learning models using...
The medical datasets have many features if the features have a tendency of mutation then the risk of disease increases which makes difficult to provide a diagnosis of disease. In the dataset, every feature is a contributor for prediction accuracy, the selection of significant features from the dataset is a challenging task. The feature selection technique based on metaheuristic algorithms is used...
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...
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, highthroughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs among the candidates set remains timeconsuming and expensive. In addition, contrasting to the number of SNPs involved, the available datasets (samples) generally have fairly small sample...
In order to improve the performance of the base classifier in the process of AdaBoost algorithm and simplify the complexity of the whole ensemble learning system, this paper presents a SVM ensemble method based on an improved iteration process of Adaboost algorithm. The improved Adaboost algorithm is added with methods of adding sample selection and feature selection in its iterative process in order...
A class imbalance problem often appears in many real world applications, e.g. fault diagnosis, text categorization, fraud detection. When dealing with a large-scale imbalanced dataset, feature selection becomes a great challenge. To confront it, this work proposes a feature selection approach based on a decision tree rule. The effectiveness of the proposed approach is verified by classifying a large-scale...
After “9.11” terrorist attacks, more advanced information technologies have been developed to counter terrorism domain to enhance the performance of early warning system. Machine learning based data mining can be applied to predict terrorist event hidden in terrorist attack events and by which the experts expect to get a clear picture of what the terrorists are thinking about in order to step up defense...
Cancer is a deadly disease in which body's cells start dividing enormously and are able to spread in to other tissues. Oral cancer is a kind of cancer, where some abnormal lesions or patches will appear in the oral cavity. Since it is difficult to identify it in the initial stages, it has one of the worst survival rates. The proposed health alert system can help the patients in identifying the disease...
Cancer diagnosis is one of the emerging applications in microarray gene expression data. Feature selection plays a crucial role because of the huge dimensionality and less training and testing samples. Finding a small subset of significant genes from a large set of gene expression data is a challenging task. This paper presents the usage of genetic algorithm as a tool to determine the informative...
Feature selection and discretization have been considered to be an important research topic in the field of pattern recognition and data mining. However, addressing both these issues at a time is rarely discussed in the existing research. In this paper, these issues have been addressed by developing a heuristic namely discretization and selection of features based on mutual information (DSM). Experimental...
Many disorders can be diagnosed by analysis of gene expression microarrays and this can save lots of lives. However, as gene expression data have high dimensions, establishing a method to identify the genes related to the target disease still remains a challenge, because it should provide a well-grounded prediction about the disease status. To this end, the best subset of genes should be distinguished...
An optimal classification model for classifying on a given problem should comprise of a classifier, a proper feature subset and a parameter set such that the classifier can attain high prediction performance as possible. Many recent feature selection methods are either too exhaustive or too greedy. Besides, many classification approaches conduct parameter search after feature selection stage, resulting...
Transcriptome data has been proved to be very valuable for clinical applications, such as diagnosis and prognosis of various cancers. In this paper, we present layer-wise feature selection in conjunction with stacked sparse auto-encoders (SSAE), a deep learning strategy for tumor classification with gene expression data. While SSAE learns high-level features from data, performing feature selection...
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...
In recent years the Android Operating System (OS) has become one of the major stakeholders in the smartphone market. The growing consumers' adoption of Android has also brought many security concerns as the number of malicious applications targeting this OS has dramatically increased. Current malware detection methods include static and dynamic analysis. In this work, a set of results obtained for...
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,...
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