Prediction of Spot Price of Iron Ore Based on PSR-WA-LSSVM Combined Model

Aiming at the problems that the existing single time series models are not accurate and robust enough when it comes to forecasting the iron ore prices and the parameters of the traditional LSSVM model are difficult to determine, we propose a combined model based on Phase Space Reconstruction (PSR), wavelet transform and LSSVM (PSR-WA-LSSVM) to tackle these issues. ARIMA model, LSTM model, PSR-LSSVM model, and PSR-WA-LSSVM models were used for contrast simulation to forecast the spot price data of 61.5%PB powder from January 30, 2019, to February 1, 2021, in Ningbo Zhoushan port. The experimental results show that the PSR-WA-LSSVM combination model achieves better prediction results. At the same time, the model has a good performance in the multistep prediction of the iron ore price

Lévy mutation in artificial bee colony algorithm for gasoline price prediction

In this paper, a mutation strategy that is based on Lévy Probabily Distribution is introduced in Artificial Bee Colony algorithm. The purpose is to better exploit promising solutions found by the bees.Such an approach is used to improve the performance of the original ABC in optimizing Least Squares Support Vector Machine hyper parameters.From the conducted experiment, the proposed lvABC shows encouraging results in optimizing parameters of interest.The proposed.lvABC-LSSVM has outperformed existing prediction model, Backpropogation Neural Network (BPNN), in predicting gasoline price

The SFA-LSSVM as a Decision Support System for Mitigating Liquefaction Disasters

Advanced data mining techniques are potential tools for solving civil engineering problems. This study proposes a novel classification system that integrates smart firefly algorithm (SFA) with least squares support vector machine (LSSVM). SFA is an optimization algorithm which combines firefly algorithm (FA) with smart components, namely chaotic logistic map, chaotic gauss/mouse map, adaptive inertia weight and Lévy flight to enhance optimization solutions. The least squares support vector machine (LSSVM) was adopted in this study for its superior performance of solving real-world problems. Based on the provided engineering data, the analytical results confirm that the SFA-LSSVM has 95.18% prediction accurac

LSSVM Model for Penetration Depth Detection in Underwater Arc Welding Process

Abstract. For underwater arc welding, it is much more complexity and difficulty to detect penetration depth than land arc welding. Based on least squares support vector machines (LSSVM), welding current, arc voltage, travel speed, contact-tube-to-work distance, and weld pool width are extracted as input units. Penetration depth is predicted in underwater flux-cored arc welding (FCAW). For improvement prediction performance, the LSSVM parameters are adaptively optimized. The experimental results show that this model can achieve higher identification precision and is more suitable to detect the depth of underwater FCAW penetration than back propagation neural networks (BPNN)

Bringing Statistical Learning Machines Together for Hydro-Climatological Predictions - Case Study for Sacramento San Joaquin River Basin, California

Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). New hydrological insights: The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the proposed approach showed better forecasting skills with preprocessed large scale climate variables rather than using the predefined indices. The proposed study is simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds

Advanced models of supervised structural clustering

The strength and power of structured prediction approaches in machine learning originates from a proper recognition and exploitation of inherent structural dependencies within complex objects, which structural models are trained to output. Among the complex tasks that benefited from structured prediction approaches, clustering is of a special interest. Structured output models based on representing clusters by latent graph structures made the task of supervised clustering tractable. While in practice these models proved effective in solving the complex NLP task of coreference resolution, in this thesis, we aim at exploring their capacity to be extended to other tasks and domains, as well as the methods for performing such adaptation and for improvement in general, which, as a result, go beyond clustering and are commonly applicable in structured prediction. Studying the extensibility of the structural approaches for supervised clustering, we apply them to two different domains in two different ways. First, in the networking domain, we do clustering of network traffic by adapting the model, taking into account the continuity of incoming data. Our experiments demonstrate that the structural clustering approach is not only effective in such a scenario, but also, if changing the perspective, provides a novel potentially useful tool for detecting anomalies. The other part of our work is dedicated to assessing the amenability of the structural clustering model to joint learning with another structural model, for ranking. Our preliminary analysis in the context of the task of answer-passage reranking in question answering reveals a potential benefit of incorporating auxiliary clustering structures. Due to the intrinsic complexity of the clustering task and, respectively, its evaluation scenarios, it gave us grounds for studying the possibility and the effect from optimizing task-specific complex measures in structured prediction algorithms. It is common for structured prediction approaches to optimize surrogate loss functions, rather than the actual task-specific ones, in or- der to facilitate inference and preserve efficiency. In this thesis, we, first, study when surrogate losses are sufficient and, second, make a step towards enabling direct optimization of complex structural loss functions. We propose to learn an approximation of a complex loss by a regressor from data. We formulate a general structural framework for learning with a learned loss, which, applied to a particular case of a clustering problem – coreference resolution, i) enables the optimization of a coreference metric, by itself, having high computational complexity, and ii) delivers an improvement over the standard structural models optimizing simple surrogate objectives. We foresee this idea being helpful in many structured prediction applications, also as a means of adaptation to specific evaluation scenarios, and especially when a good loss approximation is found by a regressor from an induced feature space allowing good factorization over the underlying structure

L2-norm multiple kernel learning and its application to biomedical data fusion

<p>Abstract</p> <p>Background</p> <p>This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as <it>L</it>_∞, <it>L</it>₁, and <it>L</it>₂MKL. In particular, <it>L</it>₂MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing <it>L</it>_∞MKL method. In real biomedical applications, <it>L</it>₂MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.</p> <p>Results</p> <p>We provide a theoretical analysis of the relationship between the <it>L</it>₂optimization of kernels in the dual problem with the <it>L</it>₂coefficient regularization in the primal problem. Understanding the dual <it>L</it>₂problem grants a unified view on MKL and enables us to extend the <it>L</it>₂method to a wide range of machine learning problems. We implement <it>L</it>₂MKL for ranking and classification problems and compare its performance with the sparse <it>L</it>_∞and the averaging <it>L</it>₁MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. <it>L</it>₂MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel <it>L</it>₂MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.</p> <p>Conclusions</p> <p>This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in <it>L</it>_∞MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing <it>L</it>₂kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.</p> <p>Availability</p> <p>The MATLAB code of algorithms implemented in this paper is downloadable from <url>http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html</url>.</p

Modeling for the Computer-Aided Design of Long Interconnects

L'abstract è presente nell'allegato / the abstract is in the attachmen