An Efficient Classification of Fuzzy XML Documents Based on Kernel ELM

2021 | Information Systems Frontiers | Citations: 0

Authors: Zhao, Zhen; Ma, Zongmin; Yan, Li

Abstract: Data classification for distributed and heterogeneous XML data sources is always ... Expand

Abstract: Data classification for distributed and heterogeneous XML data sources is always an open challenge. A considerable number of algorithms for classification of XML documents have been proposed in the literature. Yet, the existing approaches fall short in ability to classify the fuzzy XML documents. In this paper, we provide a KPCA-KELM classification framework for the fuzzy XML documents based on Kernel Extreme Learning Machine (KELM). Firstly, we propose a novel fuzzy XML document tree model to represent fuzzy XML documents. Secondly, we employ an effective vector space model to represent the semantic structure of fuzzy XML documents based on the proposed fuzzy XML document tree model. Thirdly, we classify the fuzzy XML document using KELM after feature extraction using Kernel Principal Component Analysis (KPCA). The corresponding experimental results demonstrate that our proposed KPCA-KELM approach shortens the training time while maintaining the same level of accuracy as the state-of-the-art baseline models. Collapse

Topics: extensible markup language Wikipedia operating system Microsoft Windows decision making

Methods: computational algorithm support vector machine machine learning experiment simulation

Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage

2012 | Decision Support Systems | Citations: 0

Authors: Sermpinis, Georgios; Dunis, Christian; Laws, Jason; Stasinakis, Charalampos

Abstract: The motivation of this paper is to investigate the use of a Neural Network (NN) ... Expand

Abstract: The motivation of this paper is to investigate the use of a Neural Network (NN) architecture, the Psi Sigma Neural Network (PSN), when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate using the European Central Bank (ECB) fixing series and to explore the utility of Kalman Filters in combining NN forecasts. This is done by benchmarking the statistical and trading performance of PSN with a Naive Strategy, an Autoregressive Moving Average (ARMA) model and two different NN architectures, a Multi-Layer Perceptron (MLP) and a Recurrent Network (RNN). We combine our NN forecasts with Kalman Filter, a traditional Simple Average, the Bayesian Average, the Granger–Ramanathan's Regression Approach (GRR) and the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, we apply a time-varying leverage strategy based on RiskMetrics volatility forecasts in order to further improve the forecasting performance of our models and combinations. The statistical and trading performance of our models is estimated throughout the period of 2002–2010, using the last two years for out-of-sample testing. In terms of our results, the PSN outperforms all models' individual performances in terms of statistical accuracy and trading performance. The forecast combinations also present improved empirical evidence, with Kalman Filters outperforming by far its benchmarks. We also note that after the application of the time varying leverage, all models except ARMA show a substantial increase in their trading performance. Collapse

Topics: artificial intelligence

Methods: autoregressive moving average model artificial neural network lasso regression longitudinal research computational algorithm

Theories: transaction cost economics

Extended Kalman Filter for wireless LAN based indoor positioning

2008 | Decision Support Systems | Citations: 0

Authors: Yim, Jaegeol; Park, Chansik; Joo, Jaehun; Jeong, Seunghwan

Abstract: A WLAN (Wireless Local Area Network) based Extended Kalman Filter (EKF) method f ... Expand

Abstract: A WLAN (Wireless Local Area Network) based Extended Kalman Filter (EKF) method for indoor positioning is introduced in this paper. WLAN based indoor positioning is more economical than other methods because it does not require any special equipment dedicated to positioning. The most popular technique used for indoor positioning is the fingerprinting method, but the EKF method is easier to deploy because, unlike fingerprinting, it does not require a time consuming off-line phase. This paper also provides experimental comparisons of our EKF method with other indoor positioning methods. Collapse

Topics: usability graphical user interface local area network decision making laptop computer

Methods: computational algorithm decision tree classification k-nearest neighbor experiment Bayesian analysis