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With the rise of network on handheld devices, security of the network has become critical issue. Intrusion detection system is used to predict intrusive packets on network; two-step procedure has been used to predict the intrusion, i.e., feature selection and then classification. Firstly, unwanted and expandable features in data lead to network classification problem which affect the decision capability of the classifiers, so we need optimize feature selection technique. Feature selection technique used in this paper is based on the correlation information known as correlation-based feature selection (CFS). In this paper, CFS's search algorithm is implemented using Chaotic Flower Pollination Algorithm (CFPA) that logically selects the most favorable features for classification referred as CFPA-CFS. Further, hybridization of CFPA and support vector machine classifier is implemented and named as CFPSVM. Finally, novel IDS framework uses CFPA-CFS and CFPSVM in sequence to predict the intrusion. Further, performance of proposed framework is evaluated using two intrusion detection evaluation datasets, namely KDDCup99 and NSL-KDD. The results demonstrate that proposed CFPA-CFS contributes more critical features for CFPSVM to achieve better accuracy compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid intelligent technique is presented based on the improvement in the feature selection method for multi-fault classification. The bearing conditions used in this study include healthy condition, defective inner ring, defective outer ring, and the faulty rolling element at different rotating motor speeds. To form the feature matrix, at first, the vibration signals are decomposed using empirical mode decomposition and wavelet packet decomposition. Then, the time and frequency domain features are extracted from the raw signals and the components are obtained from the signal decomposition. The high-dimensional feature matrix leads to increasing the computational complexity and reducing the efficiency in the classification accuracy of faults. Therefore, in the first stage of the feature selection process, the redundant and unnecessary features are eliminated by the FDAF-score feature selection method and the preselected feature set is formed. The FDAF-score technique is a combination of both F-score and Fisher discriminate analysis (FDA) algorithms. Since there may exist the features that are not susceptible to the presence of faults, the binary particle swarm optimization (BPSO) algorithm and the support vector machine (SVM) are used to select the optimal features from the preselected features. The BPSO algorithm is used to determine the optimal feature set and SVM classifier parameters so that the predictive error of the bearing conditions and the number of selected features are minimized. The results obtained in this paper demonstrate that the selected features are able to differentiate the different bearing conditions at various speeds. Comparing the results of this article with other fault detection methods indicates the ability of the proposed method. [ABSTRACT FROM AUTHOR]

Document representation is a key problem in document analysis and processing tasks, such as document classification, clustering and information retrieval. Especially for unstructured text data, the use of a suitable document representation method would affect the performance of the subsequent algorithms for applications and research. In this paper, we propose a novel document representation method called the conditional co-occurrence degree matrix document representation method (CCODM), which is based on word co-occurrence. CCODM not only considers the co-occurrence of terms but also considers the conditional dependencies of terms in a specific context, which leads to more available and useful structural and semantic information being retained from the original documents. Extensive experimental classification results with different supervised and unsupervised feature selection methods show that the proposed method, CCODM, achieves better performance than the vector space model, latent Dirichlet allocation, the general co-occurrence matrix representation method and the document embedding method. [ABSTRACT FROM AUTHOR]