Data Mining - Concepts and Techniques.

Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller sets of labelled data. In recent years, research in this area has followed the general trends observed in machine learning, with much attention directed at neural network-based models and generative learning. The literature on the topic has also expanded in volume and scope, now encompassing a broad spectrum of theory, algorithms and applications. However, no recent surveys exist to collect and organize this knowledge, impeding the ability of researchers and engineers alike to utilize it. Filling this void, we present an up-to-date overview of semi-supervised learning methods, covering earlier work as well as more recent advances. We focus primarily on semi-supervised classification, where the large majority of semi-supervised learning research takes place. Our survey aims to provide researchers and practitioners new to the field as well as more advanced readers with a solid understanding of the main approaches and algorithms developed over the past two decades, with an emphasis on the most prominent and currently relevant work. Furthermore, we propose a new taxonomy of semi-supervised classification algorithms, which sheds light on the different conceptual and methodological approaches for incorporating unlabelled data into the training process. Lastly, we show how the fundamental assumptions underlying most semi-supervised learning algorithms are closely connected to each other, and how they relate to the well-known semi-supervised clustering assumption.

/pdf/a-survey-on-semi-supervised-learning-3dmgrjzz6b.pdf

A survey on semi-supervised learning

Feature selection in machine learning: A new perspective

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

/pdf/deep-learning-in-mobile-and-wireless-networking-a-survey-ct8cy2kuqi.pdf

Deep Learning in Mobile and Wireless Networking: A Survey

Pervasive healthcare systems, smart grids, and unmanned aircraft systems are examples of Cyber-Physical Systems (CPSs) that have become highly integrated in the modern world. As this integration deepens, the importance of securing these systems increases. In order to identify gaps and propose research directions in CPS intrusion detection research, we survey the literature of this area. Our approach is to classify modern CPS Intrusion Detection System (IDS) techniques based on two design dimensions: detection technique and audit material. We summarize advantages and drawbacks of each dimension’s options. We also summarize the most and least studied CPS IDS techniques in the literature and provide insight on the effectiveness of IDS techniques as they apply to CPSs. Finally, we identify gaps in CPS IDS research and suggest future research areas.

/pdf/a-survey-of-intrusion-detection-techniques-for-cyber-51fb5blgwd.pdf

A survey of intrusion detection techniques for cyber-physical systems

A Survey on semi-supervised feature selection methods

Graphical abstractDisplay Omitted HighlightsA PSO-KDE model is proposed to diagnosis of breast cancer.PSO is used to determine bandwidth and select feature subset in KDE based classifier.PSO-KDE solves the multiple criteria problem using two single objective functions.The aim is to minimize the number of features and the classification error. Machine learning techniques can be used in diagnosis of breast cancer to help pathologists and physicians for decision making process. Kernel density estimation is a popular non-parametric method which can be applied for the estimation of data in many diverse applications. Selection of bandwidth and feature subset in kernel density estimator significantly influences the classification performance. In this paper, a PSO-KDE model is proposed that hybridize the particle swarm optimization (PSO) and non-parametric kernel density estimation (KDE) based classifier to diagnosis of breast cancer. In the proposed model, particle swarm optimization is used to simultaneously determine the kernel bandwidth and select the feature subset in the kernel density estimation based classifier. Classification performance and the number of selected features are the criteria used to design the objective function of PSO-KDE. The performance of the PSO-KDE is examined on Wisconsin Breast Cancer Dataset (WBCD) and Wisconsin Diagnosis Breast Cancer Database (WDBC) using classification accuracy, sensitivity and specificity. Experimental results demonstrate that the proposed model has better average performance than GA-KDE model in diagnosis of breast cancer.

Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer

Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks. In this paper, a Noise-Robust Convolutional Neural Network (NR-CNN) is proposed to classify the noisy images without any preprocessing for noise removal and improve the classification performance of noisy images in convolutional neural networks. In the proposed NR-CNN, a noise map layer and an adaptive resize layer are added to the architecture of convolutional neural network. Moreover, the noise problem is considered in different components of NR-CNN such that convolutional layer, pooling layer and loss function of the convolutional neural network are improved for robustness of CNN to noise. The adaptive data augmentation based on noise map are introduced to improve the classification performance of the proposed NR-CNN. Experimental results demonstrate that the proposed NR-CNN improves the noisy image classification and the network training speed.

A noise robust convolutional neural network for image classification

Wireless Sensor Network is one of the new technologies that have gotten more attention in the past few years. The localization problem is one of the most important topics in these types of the networks. The traditional positioning techniques cannot be used in these networks due to the hardware restrictions of the sensor nodes. Lately, some positioning methods which use soft computing approaches such as neural networks, are proposed for solving the localization problem. In this paper, we propose a new range-free localization algorithm which uses the neural networks for this purpose. This method utilizes Particle swarm optimization (PSO) algorithm to optimize the number of neurons of hidden layers of neural networks. The objective function considers both localization accuracy and storage overhead, simultaneously. The proposed algorithm is implemented and simulated in isotropic networks with and without coverage hole, and anisotropic networks. The obtained result show, in the different environmental conditions, the proposed algorithm has a less localization error rate and less storage requirement than the analogous methods.

Mehdi Agha Sarram

Papers

A Survey on semi-supervised feature selection methods

Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer

A noise robust convolutional neural network for image classification

A New Range-Free and Storage-Efficient Localization Algorithm Using Neural Networks in Wireless Sensor Networks

Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems