Improved Visual Clustering through Unsupervised Dimensionality Reduction

Network traffic in cloud computing is characterized by a large volume of data, with potential high dimensionality and high levels of redundancy. “Big data” not only retard the execution process of intrusion detection systems (IDS) but they can also lead to unsatisfactory classification accuracy. The efficient correlation-based feature selection (ECOFS) approach proposed in this paper, can handle linearly and nonlinearly dependent data and it can eliminate redundant and irrelevant features. Its effectiveness has been evaluated through its employment in an intrusion detection system. A Libsvm-IDS has been built to operate using the features selected by the proposed ECOFS algorithm. The performance of the hybrid Libsvm-IDS + ECOFS approach has been evaluated using two well-known intrusion detection evaluation datasets, namely the KDD Cup 99 and the NSL-KDD. The evaluation results show that our ECOFS algorithm selects the smallest number of features, resulting in the lowest computational cost for the Libsvm-IDS, without any performance compromise. In fact, our algorithm has achieved higher accuracy compared with two well established methods.

A simple and efficient filter feature selection method via document-term matrix unitization

Feature selection is a key step for activity classification applications. Feature selection selects the most relevant features and considers how to use each of the selected features in the most suitable format. This paper proposes an efficient feature selection method that organizes multiple subsets of features in a multilayer, rather than utilizing all selected features together as one large feature set. The proposed method was evaluated by 13 subjects (aged from 23 to 50) in a lab environment. The experimental results illustrate that the large number of features (3 vs. 7 features) are not associated with high classification accuracy using a single Support Vector Machine (SVM) model (61.3% vs. 44.7%). However, the accuracy was improved significantly (83.1% vs. 44.7%), when the selected 7 features were organized as 3 subsets and used to classify 10 postures (9 motionless with 1 motion) in 3 layers via a hierarchical algorithm, which combined a rule-based algorithm with 3 independent SVM models.

BackgroundPresently, there is a wide variety of classification methods and deep neural network approaches in bioinformatics. Deep neural networks have proven their effectiveness for classification tasks, and have outperformed classical methods, but they suffer from a lack of interpretability. Therefore, these innovative methods are not appropriate for decision support systems in healthcare. Indeed, to allow clinicians to make informed and well thought out decisions, the algorithm should provide the main pieces of information used to compute the predicted diagnosis and/or prognosis, as well as a confidence score for this prediction.MethodsHerein, we used a new supervised autoencoder (SAE) approach for classification of clinical metabolomic data. This new method has the advantage of providing a confidence score for each prediction thanks to a softmax classifier and a meaningful latent space visualization and to include a new efficient feature selection method, with a structured constraint, which allows for biologically interpretable results.ResultsExperimental results on three metabolomics datasets of clinical samples illustrate the effectiveness of our SAE and its confidence score. The supervised autoencoder provides an accurate localization of the patients in the latent space, and an efficient confidence score. Experiments show that the SAE outperforms classical methods (PLS-DA, Random Forests, SVM, and neural networks (NN)). Furthermore, the metabolites selected by the SAE were found to be biologically relevant.ConclusionIn this paper, we describe a new efficient SAE method to support diagnostic or prognostic evaluation based on metabolomics analyses.

Recent advances and emerging challenges of feature selection in the context of big data

Power systems are being operated close to their voltage security limit which makes their reliable operation more challenging. Voltage stability has been a major problem faced by utilities now a days. Many blackout have been reported around the world related to voltage instability problem. Many voltage stability analysis methods have been presented up till now. Here artificial neural network based approach for voltage stability analysis of power system is presented. Voltage stability analysis of power system has been done in terms of voltage stability margin. Voltage stability margin is calculated by estimating distance from current operating point to maximum voltage stability point according to loading parameter. Principal Component Analysis is an efficient feature selection method used here to find few input variables required to estimate voltage stability margin with sufficient accuracy and execution speed. Major drawback of previously published work is they need to train new neural network whenever change in system topology occur which is very time consuming process. Therefor in this paper single ANN has been prepared for estimating VSM for several system configuration. Feed forward neural network and radial basis function neural network have been used for voltage stability analysis of power system. The final result indicate that proposed method provide an efficient and compact ANN model for different system configuration as well as base case operating condition with lowest possible input required.

An efficient unsupervised feature selection method based on unsupervised optimal discriminant vector is developed to find the important features without using class labels. Features are ranked according to the feature importance measurement based on unsupervised optimal discriminant vector in the following steps. First, fuzzy Fisher criterion is adopted as objective function to derive the optimal discriminant vector in unsupervised pattern. Second, the feature importance measurement based on elements of unsupervised optimal discriminant vector is defined to determine the importance of each feature. The features with little importance measurement are removed from the feature subset. Experiments on UCI dataset and fault diagnosis are carried out to show that the proposed method is very efficient and able to deliver reliable results.

In this paper, PCA based modeling of geometric structure of the face for automatic face detection is presented. The method improves the face detection rate and limits the search space. Skin Color Modeling (SCM) is one of the best face detection techniques for image and video. However, feature selection is very important for even better template matching performance in terms of detection rate and time. This paper presents an efficient feature extraction and selection method based on geometric structure of the facial image boundary and interior. To model the geometric structure of face, Principle Component Analysis (PCA) and canny edge detection are used. Fusion of PCA based geometric modeling and SCM method provides higher face detection accuracy and improves time complexity. Both models provide filtering of image in term of pixel values to get the face location that are very fast and efficient for large image databases.

Objectives: To present a model which can detect malicious attacks using the URL of the Social Communication Platform using the data imbalance machine learning algorithm. The main objective is to detect the attack and prevent it from happening. Methods: This study presents an efficient feature extraction and selection method addressing feature imbalance problems; and also presents an improved concept drift and machine learning-based classification. This paper extracts the URL of the undesired tweets, identifies them, and filters them for classification. Findings: The experiments have been conducted using the drifted twitter spam dataset. Our model DIA-XGBoost extracts the URL of the undesired tweets, identifies them, and filters them for classification. Further, the attack pattern varies with respect to time. Furthermore, the results show that our DIA-XGBoost attains higher accuracy performance by 1.254%, URL recall performance by 0.14%, and increased Fmeasure performance by 10% when compared with the existing ML techniques (Random Forest, K-Nearest Neighbour, XGBoost). Thus, the existing ML-based classification model achieves poor classification accuracy whereas our model solves this issue. Novelty: Various Machine Learning (ML) techniques have been applied for the classification of URL redirection attacks. However, the spam data generally exhibit feature imbalance. Further, the attack pattern varies with respect to time. Thus, the existing ML-based classification model achieves poor classification accuracy. Hence, our model solves the issue using the DIA-XGBoost algorithm, detects and prevents URL malicious attacks. Keywords: Data Imbalance; Feature Extraction; Concept Drift; URL; Machine Learning; URL Redirection Attack

Graph technology emerges as an important topic in the field of data mining and machine learning community. The analysis of high-dimensional data is crucial to identify a smaller subset of features which are informative for classification and clustering. In this paper, an efficient graph feature selection method is proposed to render the analysis of high-dimensional data tractable. Here, the feature scores are calculated for obtaining the weights of the edges in the weighted graph to identify the optimal feature subset. One advantage of this method is that it can successfully identify the optimal features for machine learning. The experimental results on our dataset verify the effectiveness and efficiency of the proposed method.

Chapter 6 Visual evoked potentials

Although the zebrafish has become an important model in visual neuroscience, little has been done to examine the processing of its higher visual centers. The purpose of this work was twofold. The first purpose was to examine the physiology of the zebrafish retinotectal system and its relationship to retinal physiology. Spectral sensitivity functions were derived from visually evoked tectal responses and these functions were compared to the functions of electroretinogram (ERG) responses obtained using the same stimulus conditions. The second purpose was to examine the recovery of visual functioning of the tectum following optic nerve damage. The optic nerves of adult zebrafish were damaged (crushed), and tectal visual processing was assessed following damage. The results showed that the spectral sensitivity functions based on the On-responses of the tectum and ERG were qualitatively similar. The functions based on each response type received similar cone contributions including both nonopponent and opponent contributions. However, the spectral sensitivity functions based on the Off-responses of the tectum and ERG differed. The results also showed that the zebrafish visual system is capable of neural regeneration. By 90 days following an optic nerve crush, the spectral sensitivity function based on the tectal On-response was similar to functions obtained from normal zebrafish. Although the tectal Off-response did recover, the spectral sensitivity based on the Off-response was not the same as the function of normal zebrafish. These results support the notion that different levels of the visual system process information differently and that the zebrafish visual system, like those of other lower vertebrates, is capable of functional regeneration.

Feature-based quality classification for ultrasonic welding of carbon fiber reinforced polymer through Bayesian regularized neural network

An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject recognition using ground reaction force measurements. Our approach relies on a local fuzzy evaluation measure with respect to patterns that reveal the adequacy of data coverage for each feature. Furthermore, FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that those features are iteratively introduced, providing the maximum additional contribution with regard to the information content given by the previously selected features. On the basis of the principles of FuzCoC, we develop two novel techniques. At Stage 1, wavelet packet (WP) decomposition of gaits is accomplished to obtain a set of discriminating frequency sub-bands. A computationally simple FS method is then applied at Stage 2, providing a compact set of powerful and complementary features, from WP coefficients. The quality of our approach is validated via comparative analysis against existing methods on gait recognition.

This paper presents a patch-based approach for pose estimation from single images using a kernelized density voting scheme. We introduce a boosting-like algorithm that models the density using a mixture of weighted 'weak' estimators. The 'weak' density estimators and corresponding weights are learned iteratively from a training set, providing an efficient method for feature selection. Given a query image, voting is performed by reference patches similar in appearance to query image patches. Locality in the voting scheme allows us to handle occlusions and reduces the size of the training set required to cover the space of possible poses and appearance. Finally, the pose is estimated as the dominant mode in the density. Multimodality can be handled by looking at multiple dominant modes. Experiments carried out on face and articulated body pose databases show that our patch-based pose estimation algorithm generalizes well to unseen examples, is robust to occlusions and provides accurate pose estimation.