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Enhanced nearest centroid model tree classifier

Discover Computing 2025年第1期28卷 1-18页

作者： Özçelik, Mehmet Hamdi Duman, Ekrem Bağrıyanık, Selami Bulkan, Serol Applied Analytics AA Istanbul Turkey Department of Industrial Engineering Marmara Üniversitesi Mühendislik Fakültesi M5 Blok Endüstri Mühendisliği Bölümü Aydınevler Mah Maltepe Istanbul 34840 Turkey Özyeğin University Istanbul Turkey Bahçeşehir UniversityWingie Enuygun Group R & D Center Technology Istanbul Turkey Department of Computer Engineering İstanbul Health and Technology University İstanbul Turkey

In this study, first, we improved an existing variant of the Nearest Centroid algorithm. In this new version, the predictive power of features and within-class variances are used as weights in distance calculation. This version is called the Enhanced Nearest Centroid (ENC). Second, we proposed a new model tree algorithm for binary classification. It is named as the Enhanced Nearest Centroid model tree (ENCMT). The model tree is built using ENC at each leaf node of the decision tree. To evaluate the performance of the new model tree, we used an independent test platform and ran the algorithm on 30 binary datasets available therein. Results showed that ENCMT improves the performance of the decision tree algorithm. We also compared ENCMT with the Logistic model tree (LMT) algorithm and showed that it outperforms LMT as well. We also designed a bagging algorithm where ENCMT is used to build a random forest. Our comparison results show that its performance is significantly better than the Random Forest (RF) algorithm. © The Author(s) 2025.

关键词： Binary classification Information value model tree Nearest centroid classifier

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Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior

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OCEAN ENGINEERING 2009年第15-16期36卷 1175-1181页

作者： Etemad-Shahidi, A. Mahjoobi, J. Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Prediction of wave height is of great importance in marine and coastal engineering. Soft computing tools such as artificial neural networks (ANNs) are recently used for prediction of significant wave height. However, ANNs are not as transparent as semi-empirical regression-based models. In addition, neural networks approach needs to find network parameters such as number of hidden layers and neurons by trial and error, which is time consuming. Therefore, in this work, model trees as a new soft computing method was invoked for prediction of significant wave height. The main advantage of model trees is that, compared to neural networks, they represent understandable rules. These rules can be readily expressed so that humans can understand them. The data set used for developing model trees comprises of wind and wave data gathered in Lake Superior from 6 April to 10 November 2000 and 19 April to 6 November 2001. M5' algorithm was employed for building and evaluating model trees. Training and testing data include wind speed (U-10) as the input variable and the significant wave height (H-s) as the output variable. Results indicate that error statistics of model trees and feed-forward back propagation (FFBP) ANNs were similar, while model trees was marginally more accurate. In addition, model tree shows that for wind speed above 4.7 m/s, the wave height increases nonlinearly by the wind speed. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Decision trees Regression tree model tree Neural network Significant wave height

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Wave runup prediction using M5′ model tree algorithm

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OCEAN ENGINEERING 2016年 112卷 76-81页

作者： Abolfathi, S. Yeganeh-Bakhtiary, A. Hamze-Ziabari, S. M. Borzooei, S. Coventry Univ Flow Measurement & Fluid Mech Res Ctr Coventry CV1 5FB W Midlands England Iran Univ Sci & Technol Sch Civil Engn Tehran 16884 Iran Inst Teknol Brunei Fac Engn Civil Engn Program Gadang Brunei Politecn Torino DIATI Cso Abruzzi 24 I-10129 Turin Italy

In recent years, soft computing schemes have received increasing attention for solving coastal engineering problems and knowledge extraction from the existing data. In this paper, capabilities of M5' Decision tree algorithm are implemented for predicting the wave runup using existing laboratory data. The decision models were established using the surf similarity parameter (xi), slope angle (cot alpha), beach permeability factor (S-p), relative wave height (H/h), wave spectrum (S-s) and wave momentum flux (m). 451 laboratory data of the wave runup were utilized for developing wave runup prediction models. The performance of developed models is evaluated with statistical measures. The results demonstrate the strength of M5' model tree algorithm in predicting the wave runup with high precision. Good agreement exists between the proposed runup formulae and existing empirical relations. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Wave runup model tree M5 ' algorithm Nearshore hydrodynamics

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model tree approach for predicting uniaxial compressive strength and Young's modulus of carbonate rocks

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2018年第1期77卷 331-343页

作者： Ghasemi, Ebrahim Kalhori, Hamid Bagherpour, Raheb Yagiz, Saffet Isfahan Univ Technol Dept Min Engn POB *** Esfahan Iran Pamukkale Univ Dept Geol Engn TR-20020 Denizli Turkey

The uniaxial compressive strength (UCS) and Young's modulus (E) of rock are important parameters for evaluating the strength, deformation, and stability of rock engineering structures. Direct measurement of these parameters is expensive, time-consuming, and even infeasible in some circumstances due to the difficulty involved in obtaining core samples. Recently, soft computing tools have been used to predict UCS and E based on index tests. Most of these tools are not as transparent and easy to use as empirical regression-based models. This study presents another soft computing approach-model trees-for predicting the UCS and E of carbonate rocks. The main advantages of model trees are that they are easier to use than other data learning tools and, more importantly, they represent understandable mathematical rules. In this study, the M5P algorithm was employed to build and evaluate model trees (UCS and E model trees). First, the models were developed in an unpruned form, and then they were pruned to avoid overfitting. The data used to train and test the model trees were collected from quarries in southwestern Turkey. model trees included Schmidt hammer, effective porosity, dry unit weight, PaEurowave velocity, and slake durability index as input variables. When the models were assessed using a number of statistical indices (RMSE, MAE, VAF, and R (2)), it was found that unpruned and pruned model trees provide acceptable predictions of UCS and E, although the pruned models are simpler and easier to understand.

关键词： Uniaxial compressive strength Young's modulus model tree M5P algorithm Index tests Carbonate rocks

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model tree approach for prediction of pile groups scour due to waves

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OCEAN ENGINEERING 2011年第13期38卷 1522-1527页

作者： Etemad-Shahidi, A. Ghaemi, N. Iran Univ Sci & Technol Sch Civil Engn Envirohydroinformat COE Tehran Iran

Scour around piles could endanger the stability of the structures placed on them. Hence, an accurate estimation of the scour depth around piles is very important in coastal and marine engineering. Due to the complex interaction between the wave, seabed and pile group;prediction of the scour depth is not an easy task and the available empirical formulas have limited accuracy. Recently, soft computing methods such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have been used for the prediction of the scour depth. However, these methods do not give enough insight about the process and are not as easy to use as the empirical equations. In this study, new formulas are given that are easy to use, accurate and physically sound. Available empirical equations for estimating the pile group scour depth such as those of Sumer et al. (1992) and Bayram and Larson (2000), are less accurate compared to the given equations. These equations are as accurate as other soft computing methods such as ANN and SVM. Moreover, in this study, safety factors are given for different levels of acceptable risks, which can be so useful for engineers. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Scour depth Pile group Soft computing method model tree Probabilistic design

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model tree pruning

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第12期10卷 3431-3444页

作者： Zhou, Xinlei Yan, Dasen Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

A model tree is a decision tree in which a specified model, such as a linear regression or naive Bayes model, is built on part of the leaf nodes. Compared with the typical decision tree in which every leaf node is assigned a class label, a model tree has several advantages: the flexibility to handle mixed attributes, a simplified tree structure, and a good potential for processing big data. This paper investigates a model tree in which the ELM model is applied to some leaf nodes of the tree and compares two fundamental strategies for generating model trees in terms of training complexity and generalization ability, namely, prepruning and postpruning. The experimental results and algorithmic analysis show that, with respect to the ELM model tree, postpruning achieves better performance than does prepruning, which has previously been universally regarded as one of the most popular decision tree generation strategies.

关键词： model tree Pruning Decision tree Extreme learning machine ELM-tree

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Application of model tree and Evolutionary Polynomial Regression for evaluation of sediment transport in pipes

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KSCE JOURNAL OF CIVIL ENGINEERING 2017年第5期21卷 1956-1963页

作者： Najafzadeh, Mohammad Laucelli, Daniele Biagio Zahiri, Abdolreza Grad Univ Adv Technol Kerman Dept Civil Engn POB 76315-116 Kerman Iran Tech Univ Bari Dept Civil Engn & Architecture Via E Orabona 4 I-71025 Bari Italy Gorgan Univ Agr Sci & Nat Resources Dept Water Engn POB 386 Gorgan Iran

Prediction of critical velocity for sediment deposition is a significant component in design of sewer pipes. Because of the abrupt changes in velocity and shear stress distributions, traditional equations based on regression analysis can fail in evaluating sediment transport efficiently. Therefore, different artificial intelligence approaches have been applied to investigate sediment transport in sewer pipes. This study proposes two different approaches to predict the critical velocity for sediment deposition in sewer networks: model tree (MT) and the Evolutionary Polynomial Regression (EPR), a hybrid data-driven technique that combines genetic algorithms with numerical regression. The hydraulic radius, average size of sediments, volumetric concentration, total friction factor, and non-dimensional sediment size were considered as input parameters to characterize sediment transport in clean sewer pipes. The present study implements data collected from different works in literature. The proposed modeling approaches are compared to some benchmark formulas from literature, and discussed from the accuracy and knowledge discovery points of view, highlighting the advantage of both proposed techniques. Results indicated that both techniques have similar accuracy in predictions, but EPR allows to physical validation of returned formulas, allowing identifying the most influent inputs on the phenomenon at stake.

关键词： model tree evolutionary polynomial regression sediment transport sewer pipes non-deposition conditions traditional methods

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Prediction of regional streamflow frequency using model tree ensembles

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JOURNAL OF HYDROLOGY 2014年 517卷 298-309页

作者： Schnier, Spencer Cai, Ximing Univ Illinois Dept Civil & Environm Engn Hydrosyst Lab 2535c Urbana IL 61801 USA

This study introduces a novel data-driven method called model tree ensembles (MTEs) to predict streamflow frequency statistics based on known drainage area characteristics, which yields insights into the dominant controls of regional streamflow. The database used to induce the models contains both natural and anthropogenic drainage area characteristics for 294 USGS stream gages (164 in Texas and 130 in Illinois). MTEs were used to predict complete flow duration curves (FDCs) of ungaged streams by developing 17 models corresponding to 17 points along the FDC. model accuracy was evaluated using ten-fold cross-validation and the coefficient of determination (R-2). During the validation, the gages withheld from the analysis represent ungaged watersheds. MTEs are shown to outperform global multiple-linear regression models for predictions in ungaged watersheds. The accuracy of models for low flow is enhanced by explicit consideration of variables that capture human interference in watershed hydrology (e.g., population). Human factors (e.g., population and groundwater use) appear in the regionalizations for low flows, while annual and seasonal precipitation and drainage area are important for regionalizations of all flows. The results of this study have important implications for predictions in ungaged watersheds as well as gaged watersheds subject to anthropogenically-driven hydrologic changes. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Flow duration curve Ungaged basin Human impact Regional regression model tree Bagging

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Effect of Pruning and Smoothing while Using M5 model tree Technique for Reservoir Inflow Prediction

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JOURNAL OF HYDROLOGIC ENGINEERING 2011年第7期16卷 563-574页

作者： Jothiprakash, V. Kote, Alka S. Indian Inst Technol Dept Civil Engn Bombay 400076 Maharashtra India

This study reports the performance of an M5 model tree (MT) and the effects of pruning and smoothing applied to reservoir inflow prediction. The full year and seasonal monthly time step MT predictions were compared with conventional univariate autoregressive integrated moving average (stochastic) models. It was found that stochastic models could not predict the future inflows in a better way, because the observed series had not followed any particular distribution. However, it was found that the stochastic models showed better improvement using a logarithmic-transformed series, but the logarithmic-transformed MT results showed otherwise. The model validation was performed using the comparison of goodness of fit measures, standard statistics, time series, and scatter plots of predicted inflows with observed inflows. The effect of pruning each leaf in the MT model was also studied. Instead of pruning all the leaves, leading to lesser predictive accuracy, selective pruning was carried out based on the importance of the processes, for example, peak and low flow. The performance of both stochastic and MT models showed that seasonal monthly prediction was superior to full-year monthly prediction because of large zero values in latter data set. Encouraging results indicated that the seasonal nontransformed selective-pruned MT models performed better and produced reliable forecasts of high and low inflows than the stochastic models. A pruned and smoothed MT model (PSMT) performed 79% better than the stochastic models in terms of mean square error (MSE). On the other hand, MSE was 98% better than the stochastic model in an unpruned and unsmoothed MT (UPUSMT) model. Because of better peak prediction by UPUSMT model, the MSE was 90% better than the PSMT models. The other advantage of an MT was having a set of equations and if-then rules to predict the inflow as well as peak inflow into the Pawana reservoir. 10.1061/(ASCE)HE.1943-5584.0000342. (C) 2011 American Society of Civil

关键词： Auto regressive integrated moving average model tree Selective pruned model tree Full year Seasonal Normal distribution Pawana reservoir India

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A coupled model tree-genetic algorithm scheme for flow and water quality predictions in watersheds

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JOURNAL OF HYDROLOGY 2008年第3-4期349卷 364-375页

作者： Preis, Ami Ostfeld, Avi Technion Israel Inst Technol Fac Civil & Environm Engn IL-32000 Haifa Israel

The rapid advance in information processing systems along with the increasing data availability have directed research towards the development of intelligent systems that evolve models of natural phenomena automatically. This is the discipline of data driven modeling which is the study of algorithms that improve automatically through experience. Applications of data driven modeling range from data mining schemes that discover general rules in large data sets, to information filtering systems that automatically learn users' interests. This study presents a data driven modeling algorithm for flow and water quality toad predictions in watersheds. The methodology is comprised of a coupled model tree-genetic algorithm scheme. The model tree predicts flow and water quality constituents while the genetic algorithm is employed for calibrating the model tree parameters. The methodology is demonstrated through base runs and sensitivity analysis for daily flow and water quality load predictions on a watershed in northern Israel. The method produced close fits in most cases, but was limited in estimating the peak flows and water quality loads. (C) 2007 Elsevier B.V. All rights reserved.

关键词： data driven modeling model tree genetic algorithm evolutionary optimization watershed water quality

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