An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Artificial neural networks in geospatial analysis

Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning paradigm and network architectures. Their use is expected to grow with increasing availability of massive data from remote sensing and mobile platforms

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks constructed from real-world data sets (a location-based social media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis --- such as community structure and motif analysis --- of the reconstructed networks

A study of the accuracy, completeness, and efficiency of artificial neural networks and related inductive learning techniques

Artificial Neural Networks (ANNs) have been an intense topic of research in the last decade. They have been viewed as black boxes, where the inputs were known and the outputs were computed, but the underlying statistics and thus reliability of the networks were not fully understood. Because of this, there has been hesitation in utilizing ANNs in automated systems such as intelligent flight control. This hesitation is diminishing, however. Individual elements of a neural network can be probed and their decision-making power assessed. In this study, a neural network is trained and then various ranking methods are used to assess the importance (saliency or decision-making power, DMP) of each input node. Then, the input data is renormalized according to the DMP input vector and fed to a general regression neural network (GRNN) for training. The accuracy of the DMP ranking methods are then compared against each other from the resulting modified GRNNs. Five ranking methods are tested and compared on four separate data sets. A series of new methods are then introduced that combine the global nonlinear regression capability of ANNs with the local averaging capability of nearest neighbor approaches, based on a weighted distance metric (WDM) provided by the saliency estimates. Two new neural stacking methods are introduced that rely on this WDM. A framework for quantifying error estimation reliability is presented and discussed. Using this framework, the predictive accuracy of MSA and DCM are compared in terms of both the modeled target function and the model\u27s confidence interval about it using a new measure called the confidence coefficient. A benchmark problem is also introduced as a generic data set for future comparison between inductive learning machines. In addition, the Scaled Conjugate Gradient algorithm (SCG) is implemented for its potential in supervised learning. Two new complexity-regularization methods derived from SCG are implemented that use saliency estimates of various features of the ANN, and are driven by feedback from the cross validation (feedback) set

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics