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Author Hughes, S.; Moreno, S.; Yushimito, W.F.; Huerta-Canepa, G. doi  openurl
  Title Evaluation of machine learning methodologies to predict stop delivery times from GPS data Type Journal Article
  Year 2019 Publication Transportation Research Part C-Emerging Technologies Abbreviated Journal Transp. Res. Pt. C-Emerg. Technol.  
  Volume 109 Issue Pages 289-304  
  Keywords Machine learning; Stop delivery time; Classification; Regression; Hazard duration; GPS  
  Abstract In last mile distribution, logistics companies typically arrange and plan their routes based on broad estimates of stop delivery times (i.e., the time spent at each stop to deliver goods to final receivers). If these estimates are not accurate, the level of service is degraded, as the promised time window may not be satisfied. The purpose of this work is to assess the feasibility of machine learning techniques to predict stop delivery times. This is done by testing a wide range of machine learning techniques (including different types of ensembles) to (1) predict the stop delivery time and (2) to determine whether the total stop delivery time will exceed a predefined time threshold (classification approach). For the assessment, all models are trained using information generated from GPS data collected in Medellin, Colombia and compared to hazard duration models. The results are threefold. First, the assessment shows that regression-based machine learning approaches are not better than conventional hazard duration models concerning absolute errors of the prediction of the stop delivery times. Second, when the problem is addressed by a classification scheme in which the prediction is aimed to guide whether a stop time will exceed a predefined time, a basic K-nearest-neighbor model outperforms hazard duration models and other machine learning techniques both in accuracy and F-1 score (harmonic mean between precision and recall). Third, the prediction of the exact duration can be improved by combining the classifiers and prediction models or hazard duration models in a two level scheme (first classification then prediction). However, the improvement depends largely on the correct classification (first level).  
  Address [Hughes, Sebastian; Moreno, Sebastian; Yushimito, Wilfredo F.; Huerta-Canepa, Gonzalo] Univ Adolfo Ibanez, Fac Engn & Sci, Vina Del Mar, Chile, Email: shughes@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0968-090x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000504780800016 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1082  
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Author Henriquez, P.A.; Ruz, G.A. pdf  doi
openurl 
  Title Noise reduction for near-infrared spectroscopy data using extreme learning machines Type Journal Article
  Year 2019 Publication Engineering Applications Of Artificial Intelligence Abbreviated Journal Eng. Appl. Artif. Intell.  
  Volume 79 Issue Pages 13-22  
  Keywords Near-infrared spectroscopy; Parallel layers; Constrained optimization; Regression; Classification  
  Abstract The near infrared (NIR) spectra technique is an effective approach to predict chemical properties and it is typically applied in petrochemical, agricultural, medical, and environmental sectors. NIR spectra are usually of very high dimensions and contain huge amounts of information. Most of the information is irrelevant to the target problem and some is simply noise. Thus, it is not an easy task to discover the relationship between NIR spectra and the predictive variable. However, this kind of regression analysis is one of the main topics of machine learning. Thus machine learning techniques play a key role in NIR based analytical approaches. Pre-processing of NIR spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena (noise) in the spectra in order to improve the regression or classification model. In this work, we propose to reduce the noise using extreme learning machines which have shown good predictive performances in regression applications as well as in large dataset classification tasks. For this, we use a novel algorithm called C-PL-ELM, which has an architecture in parallel based on a non-linear layer in parallel with another non-linear layer. Using the soft margin loss function concept, we incorporate two Lagrange multipliers with the objective of including the noise of spectral data. Six real-life dataset were analyzed to illustrate the performance of the developed models. The results for regression and classification problems confirm the advantages of using the proposed method in terms of root mean square error and accuracy.  
  Address [Henriquez, Pablo A.; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Las Torres 2640, Santiago, Chile, Email: pabhenriquez@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0952-1976 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000459524300002 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 984  
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Author Henriquez, P.A.; Ruz, G.A. pdf  doi
openurl 
  Title A non-iterative method for pruning hidden neurons in neural networks with random weights Type Journal Article
  Year 2018 Publication Applied Soft Computing Abbreviated Journal Appl. Soft. Comput.  
  Volume 70 Issue Pages 1109-1121  
  Keywords Non -iterative learning; Neural networks; Random weights; Garson's algorithm; Pruning; Regression; Classification  
  Abstract Neural networks with random weights have the advantage of fast computational time in both training and testing. However, one of the main challenges of single layer feedforward neural networks is the selection of the optimal number of neurons in the hidden layer, since few/many neurons lead to problems of underfitting/overfitting. Adapting Garson's algorithm, this paper introduces a new efficient and fast non-iterative algorithm for the selection of neurons in the hidden layer for randomization based neural networks. The proposed approach is divided into three steps: (1) train the network with h hidden neurons, (2) apply Garson's algorithm to the matrix of the hidden layer, and (3) perform pruning reducing hidden layer neurons based on the harmonic mean. Our experiments in regression and classification problems confirmed that the combination of the pruning technique with these types of neural networks improved their predictive performance in terms of mean square error and accuracy. Additionally, we tested our proposed pruning method with neural networks trained under sequential learning algorithms, where Random Vector Functional Link obtained, in general, the best predictive performance compared to online sequential versions of extreme learning machines and single hidden layer neural network with random weights. (C) 2018 Elsevier B.V. All rights reserved.  
  Address [Henriquez, Pablo A.; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Las Torres 2640, Santiago, Chile, Email: pabhenriquez@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Elsevier Science Bv Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1568-4946 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000443296000077 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 912  
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Author Henriquez, P.A.; Ruz, G.A. pdf  doi
openurl 
  Title Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers Type Journal Article
  Year 2017 Publication Neurocomputing Abbreviated Journal Neurocomputing  
  Volume 226 Issue Pages 109-116  
  Keywords Extreme learning machine; Low-discrepancy points; Parallel layers; Regression; Classification  
  Abstract Extreme learning machine (ELM) is a machine learning technique based on competitive single-hidden layer feedforward neural network (SLFN). However, traclitional ELM and its variants are only based on random assignment of hidden weights using a uniform distribution, and then the calculation of the weights output using the least-squares method. This paper proposes a new architecture based on a non-linear layer in parallel by another non-linear layer and with entries of independent weights. We explore the use of a deterministic assignment of the hidden weight values using low-discrepancy sequences (LDSs). The simulations are performed with Halton and Sobol sequences. The results for regression and classification problems confirm the advantages of using the proposed method called PL-ELM algorithm with the deterministic assignment of hidden weights. Moreover, the PL-ELM algorithm with the deterministic generation using LDSs can be extended to other modified ELM algorithms.  
  Address [Henriquez, Pablo A.; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Las Torres 2640, Santiago, Chile, Email: pabhenriquez@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Elsevier Science Bv Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0925-2312 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000392037800012 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 687  
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Author Simon, F.; Ordonez, J.; Reddy, T.A.; Girard, A.; Muneer, T. pdf  doi
openurl 
  Title Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data Type Journal Article
  Year 2016 Publication Renewable Energy Abbreviated Journal Renew. Energy  
  Volume 95 Issue Pages 413-421  
  Keywords GSHP (ground-source heat pump); Performance prediction; Manufacturer data; Multiple regression (MR)  
  Abstract The performance of ground-source heat pumps (GSHP), often expressed as Power drawn and/or the COP, depends on several operating parameters. Manufacturers usually publish such data in tables for certain discrete values of the operating fluid temperatures and flow rates conditions. In actual applications, such as in dynamic simulations of heat pump system integrated to buildings, there is a need to determine equipment performance under operating conditions other than those listed. This paper describes a simplified methodology for predicting the performance of GSHPs using multiple regression (MR) models as applicable to manufacturer data. We find that fitting second-order MR models with eight statistically significant x-variables from 36 observations appropriately selected in the manufacturer catalogue can predict the system global behavior with good accuracy. For the three studied GSHPs, the external prediction error of the MR models identified following the methodology are 0.2%, 0.9% and 1% for heating capacity (HC) predictions and 2.6%, 4.9% and 3.2% for COP predictions. No correlation is found between residuals and the response, thus validating the models. The operational approach appears to be a reliable tool to be integrated in dynamic simulation codes, as the method is applicable to any GSHP catalogue data. (C) 2016 Elsevier Ltd. All rights reserved.  
  Address [Simon, F.; Ordonez, J.] Univ Granada, Sch Civil Engn, Av Severo Ochoa S-N, E-18071 Granada, Spain, Email: fsimon@correo.ugr.es  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0960-1481 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000377311000036 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 629  
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