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In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications. In contrast to traditional binary and multi-class classification, multi-label classification involves association of each of the input samples with a set of target labels simultaneously. There are no real-time online neural network based multi-label classifier available in the literature. In this paper, we exploit the inherent nature of high speed exhibited by the extreme learning machines to develop a novel online real-time classifier for multi-label data streams. The developed classifier is experimented with datasets from different application domains for consistency, performance and speed. The experimental studies show that the proposed method outperforms the existing state-of-the-art techniques in terms of speed and accuracy and can classify multi-label data streams in real-time., 8 pages, 7 tables, 3 figures. arXiv admin note: text overlap with arXiv:1609.00086

Keeping computer reliability to confirm reliable, secure, and truthful correspondence of data between different enterprises is a major security issue. Ensuring information correspondence over the web or computer grids is always under threat of hackers or intruders. Many techniques have been utilized in intrusion detections, but all have flaws. In this paper, a new hybrid technique is proposed, which combines the Ensemble of Feature Selection (EFS) algorithm and Teaching Learning-Based Optimization (TLBO) techniques. In the proposed, EFS-TLBO method, the EFS strategy is applied to rank the features for choosing the ideal best subset of applicable information, and the TLBO is utilized to identify the most important features from the produced datasets. The TLBO algorithm uses the Extreme Learning Machine (ELM) to choose the most effective attributes and to enhance classification accuracy. The performance of the recommended technique is evaluated in a benchmark dataset. The experimental outcomes depict that the proposed model has high predictive accuracy, detection rate, false-positive rate, and requires less significant attributes than other techniques known from the literature.

The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprise data from different structured, semi-structured, and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity, and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modeling of this vast heterogeneous volume of data. This paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern, are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine, an extreme learning machine, and a Gaussian mixture model are used as the classifier. In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement.

The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms.

Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge helps to classify the unstructured data. In this paper, we propose a framework CUIL (Classification of Unstructured data using Incremental Learning) which clusters the metadata, assigns a label for each cluster and then creates a model using Extreme Learning Machine (ELM), a feed-forward neural network, incrementally for each batch of data arrived. The proposed framework trains the batches separately, reducing the memory resources, training time significantly and is tested with metadata created for the standard image datasets like MNIST, STL-10, CIFAR-10, Caltech101, and Caltech256. Based on the tabulated results, our proposed work proves to show greater accuracy and efficiency.

Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN) with radial basis function (RBF) hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM) literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM) for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

In this paper we propose an algorithm for Single-hidden Layer Feedforward Neural networks training. Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection process to a low-dimensional space where classification is performed by a linear classifier, we extend the Extreme Learning Machine (ELM) algorithm in order to exploit the training data dispersion in its optimization process. The proposed Minimum Variance Extreme Learning Machine classifier is evaluated in human action recognition, where we compare its performance with that of other ELM-based classifiers, as well as the kernel Support Vector Machine classifier.