Online class imbalance learning is a new learning problem that combines the challenges of both online learning and class imbalance learning. It deals with data streams having very skewed class distributions. This type of problems commonly exists in real-world applications, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. In our earlier work, we defined class imbalance online, and proposed two learning algorithms OOB and UOB that build an ensemble model overcoming class imbalance in real time through resampling and time-decayed metrics. In this paper, we further improve the resampling strategy inside OOB and UOB, and look into their performance in both static and dynamic data streams. We give the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status. We find that UOB is better at recognizing minority-class examples in static data streams, and OOB is more robust against dynamic changes in class imbalance status. The data distribution is a major factor affecting their performance. Based on the insight gained, we then propose two new ensemble methods that maintain both OOB and UOB with adaptive weights for final predictions, called WEOB1 and WEOB2. They are shown to possess the strength of OOB and UOB with good accuracy and robustness.

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Resampling-Based Ensemble Methods for Online Class Imbalance Learning

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

A survey towards an integration of big data analytics to big insights for value-creation

Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients.

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Unmanned Aerial System (UAS)-Based Phenotyping of Soybean using Multi-sensor Data Fusion and Extreme Learning Machine

Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems ELM is based on empirical risk minimization theory and its learning process needs only a single iteration The algorithm avoids multiple iterations and local minimization It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future

Extreme learning machine: algorithm, theory and applications

Extreme learning machine is a new learning algorithm for the single hidden layer feedforward neural networks (SLFNs). ELM has been widely used in various fields and applications to overcome the slow training speed and over-fitting problems of the conventional neural network learning algorithms. ELM algorithm is based on the empirical risk minimization, without considering the structural risk and this may lead to over-fitting problems and at the same time, it is with poor controllability and robustness. For these deficiencies, an optimization method is proposed in this paper, a novel extreme learning machine based on hybrid kernel function (HKELM). The method constructs a hybrid kernel function with better performance by fully combining local kernel function strong learning ability and global kernel function strong generalization ability. Compared with traditional ELM, the results show that this method can effectively improve the ELM classification results, avoid local minimum, with better generalization, robustness, controllability and faster learning rate.

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A Novel Extreme Learning Machine Based on Hybrid Kernel Function

As is known, the extreme learning machine (ELM) algorithm is a new learning algorithm for the single hidden layer feedforward neural networks (SLFNs) This paper combines rough sets theory with extreme learning machine and proposes a new prediction algorithm of extreme learning machines based on rough sets theory Firstly, using the rough sets theory to do attribute reduction, and then using ELM to train and predict the new datasets Verified by the final experimental results and data analysis we know that compared with the traditional ELM the proposed algorithm has higher prediction accuracy and better efficiency

Yanan Zhang

Papers

Extreme learning machine: algorithm, theory and applications

A Novel Extreme Learning Machine Based on Hybrid Kernel Function

An Algorithm Research for Prediction of Extreme Learning Machines Based on Rough Sets

Rule-based adversarial sample generation for text classification