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Building optimal regression tree by ant colony system-genetic algorithm: Application to modeling of melting points

ANALYTICA CHIMICA ACTA 2011年第1-2期704卷 57-62页

作者： Hemmateenejad, Bahram Shamsipur, Mojtaba Zare-Shahabadi, Vali Akhond, Morteza Shiraz Univ Dept Chem Shiraz Iran Shiraz Univ Med Sci Med & Nat Prod Chem Res Ctr Shiraz Iran Razi Univ Dept Chem Kermanshah Iran Islamic Azad Univ Mahshahr Branch Mahshahr Iran

The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a metaheuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Ant colony system classification and regression tree Genetic algorithm Melting points Quantitative structure-property relationship

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A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine

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ACTA GEOPHYSICA 2019年第4期67卷 1025-1037页

作者： Hoang Nguyen Xuan-Nam Bui Moayedi, Hossein Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Pho Vien Hanoi Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res Duc Thang Ward 18 Pho Vien Hanoi Vietnam Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam

Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that significantly affect the environment, especially ground vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced ground vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict ground vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected;80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models' performances. Mean absolute error (MAE), determination coefficient (R-2), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced ground vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting ground vibration with an RMSE of 0.508, R-2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878;R-2 of 0.886, 0.618, 0.737;and MAE of 0.659, 1.631, 0.762, respectively.

关键词： Support vector machine classification and regression tree k-nearest neighbor Artificial neural network Ground vibration Open-pit mine

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Within-yard habitat use by white-tailed deer at varying winter severity

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FOREST ECOLOGY AND MANAGEMENT 2003年第2-3期172卷 173-182页

作者： Morrison, SF Forbes, GJ Young, SJ Lusk, S Univ New Brunswick New Brunswick Cooperat Fish & Wildlife Res Unit Fredericton NB E3B 6C2 Canada Fraser Papers Inc Edmundston NB E3V 1S9 Canada Dept Nat Resources & Energy Fish & Wildlife Branch Edmundston NB E3B 5H1 Canada

Vegetative, topological, and spatial features that were associated with habitat use of radio-collared white-tailed deer (Odocoileus virginianus, n = 17 in 1996-1997 and n = 13 in 1997-1998) were identified as snow depth changed in the Odell Deeryard, NB, Canada. Logistic regressions and classification trees revealed that use of forest stands varied with snow depth, and between the 1996-1997 and 1997-1998 yarding periods. Stand use at all snow depths was inversely related to the distance to stands with >50% softwood canopy cover (SCC). Stand use during periods of low snow depth (<20 cm) also was predicted by features such as the basal area of spruce (Picea spp. A. Dietr.) <2.5 m(2)/ha and browse availability >10% of ground cover. At moderate snow depths (2050 cm) deer were more likely to use stands within 290 m of 30-50% SCC, within 215 m of >50% SCC, and having low slopes (<4.5degrees). During periods of deep snow (>50 cm) only those features that described shelter were useful in predicting habitat use variation between years. Deer were not associated with browse-related features when snow depth exceeded 50 cm. In 1996-1997, a winter of average snow depth, deer were more likely to use stands with low perimeter:area ratios (<0.04 m/m(2)), within 220 m of cedar/hemlock-dominated (Thuja occidentalis L./Tsuga canadensis (L.) Carr.) stands, and with low basal areas of pine/larch (<0.3 m(2)/ha, Pinus spp. L./Larix laricina (Du Roi) K. Koch). In 1997-1998, an exceptionally harsh winter, deer left the yard in late January and moved to nearby timber harvest operations. However, while in the yard, deer selected stands at lower elevations (<180 m), but did not differentiate between softwood shelter species. Results suggest that deer use stands based on the spatial arrangement of shelter within the deeryard, and not just that of the individual stand. We suggest that the supply of browse during winter should be an explicit part of any deeryard management plan. (C) 2002 Elsevier

关键词： browse classification and regression tree deeryard habitat radio-telemetry shelter white-tailed deer winter severity

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Exploring correlations in gene expression microarray data for maximum predictive-minimum redundancy biomarker selection and classification

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第10期43卷 1437-1443页

作者： Arevalillo, Jorge M. Navarro, Hilario Univ Nacl Educ Distancia Dept Stat Operat Res & Numer Anal E-28040 Madrid Spain

An important issue in the analysis of gene expression microarray data is concerned with the extraction of valuable genetic interactions from high dimensional data sets containing gene expression levels collected for a small sample of assays. Past and ongoing research efforts have been focused on biomarker selection for phenotype classification. Usually, many genes convey useless information for classifying the outcome and should be removed from the analysis;on the other hand, some of them may be highly correlated, which reveals the presence of redundant expressed information. In this paper we propose a method for the selection of highly predictive genes having a low redundancy in their expression levels. The predictive accuracy of the selection is assessed by means of classification and regression trees (CART) models which enable assessment of the performance of the selected genes for classifying the outcome variable and will also uncover complex genetic interactions. The method is illustrated throughout the paper using a public domain colon cancer gene expression data set. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Microarray data Gene expression Biomarker selection classification and prediction Redundancy classification and regression tree

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A novel dendrochronological approach reveals drivers of carbon sequestration in tree species of riparian forests across spatiotemporal scales

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SCIENCE OF THE TOTAL ENVIRONMENT 2017年 574卷 1261-1275页

作者： Rieger, Isaak Kowarik, Ingo Cherubini, Paolo Cierjacks, Arne Tech Univ Berlin Dept Ecol Ecosyst Sci Plant Ecol Rothenburgstr 12 D-12165 Berlin Germany Berlin Brandenburg Inst Adv Biodivers Res BBIB D-14195 Berlin Germany WSL Swiss Fed Inst Forest Snow & Landscape Res Zurcherstr 111 CH-8903 Birmensdorf Switzerland Univ Hamburg Bioctr Klein Flottbek Biodivers Useful Plants Ohnhorststr 18 D-22609 Hamburg Germany

Aboveground carbon (C) sequestration in trees is important in global C dynamics, but reliable techniques for its modeling in highly productive and heterogeneous ecosystems are limited. We applied an extended dendrochronological approach to disentangle the functioning of drivers from the atmosphere (temperature, precipitation), the lithosphere (sedimentation rate), the hydrosphere (groundwater table, river water level fluctuation), the biosphere (tree characteristics), and the anthroposphere (dike construction). Carbon sequestration in aboveground biomass of riparian Quercus robur L and Fraxinus excelsior L was modeled (1) over time using boosted regression tree analysis (BRT) on cross-datable trees characterized by equal annual growth ring patterns and (2) across space using a subsequent classification and regression tree analysis (CART) on cross-datable and not cross-datable trees. While C sequestration of cross-datable Q robur responded to precipitation and temperature, cross-datable F. excelsior also responded to a low Danube river water level. However, CART revealed that C sequestration over time is governed by tree height and parameters that vary over space (magnitude of fluctuation in the groundwater table, vertical distance to mean river water level, and longitudinal distance to upstream end of the study area). Thus, a uniform response to climatic drivers of aboveground C sequestration in Q robur was only detectable in trees of an intermediate height class and in taller trees (>21.8 m) on sites where the groundwater table fluctuated little <= 0.9 m). The detection of climatic drivers and the river water level in F. excelsior depended on sites at lower altitudes above the mean river water level (<= 2.7 m) and along a less dynamic downstream section of the study area. Our approach indicates unexploited opportunities of understanding the interplay of different environmental drivers in aboveground C sequestration. Results may support species-specific and locally

关键词： Boosted regression tree Carbon sequestration classification and regression tree Climate Danube River Dendrochronology

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The Knowledge Discovery of the Nuclear Power Issue Using: the Artificial Intelligence Model: An Example of the CART and the SVM

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JOURNAL OF INTERNET TECHNOLOGY 2016年第6期17卷 1071-1082页

作者： Chung, Yueh-Shiun Ho, Mei Hsiu-Ching Huang, Sun-Jen Liang, Shih-Wu Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan Natl Taiwan Univ Sci & Technol Grad Inst Technol Management Taipei Taiwan Shih Hsin Univ Dept Publ Relat & Advertising Taipei Taiwan

After the nuclear accidents in the Fukushima Daiichi Nuclear Power Plant, the public has become increasingly concerned about nuclear power policies. We employed the CART and the SVM methods to establish and compare classification patterns in order to reveal obscure information in the survey data of Nuclear Power Policy'. Our results showed that the total classification accuracy of the CART is higher than that of SVM or logistic regression. 48.87% of the Taiwanese public opposed nuclear power, 34.16% supported nuclear power, and 16.97% did not respond to the question. However, the CART analysis showed that among the non-respondents, 56% supported nuclear power and 44% opposed its use. The two primary factors influencing the opinion for nuclear power are "satisfaction with the safety of nuclear power" and "improvement in safety inspection and disaster prevention standards in nuclear power plants." Therefore, by improving the safety of nuclear power plant, the government might regain public support for nuclear power.

关键词： Artificial intelligence classification and regression tree Support vector machine Logistic regression Nuclear power issue

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A data mining based approach for travel time prediction in freeway with non-recurrent congestion

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NEUROCOMPUTING 2014年 133卷 74-83页

作者： Li, Chi-Sen Chen, Mu-Chen Natl Chiao Tung Univ Dept Transportat & Logist Management Taipei 100 Taiwan

This study integrates three data mining techniques, K-means clustering, decision trees, and neural networks, to predict the travel time of freeway with non-recurrent congestion. By creating dummy variables and identifying important variables, not only is the prediction performance increased without increasing investment in equipment, but also important variables are obtained concerning the important locations of equipment in order to effectively assist public transit agencies with system maintenance. The experimental results for a segment of 36.1 km of National Freeway No. 1, Taiwan, with non-recurrent congestion show that, whether or not the data generated by the Electronic Toll Collection (etc) system is used as input variables, the travel time prediction method developed in this study is able to improve the prediction performance. Meanwhile, the proposed approach also reduces the percentage of samples with mean absolute percentage error (MAPE) > 20%. Furthermore, in this study, important variables are extracted from the decision tree in order to predict the travel time. Finally, the prediction models constructed in accordance with six scenarios are highly accurate due to the low MAPE values, which are from 6% to 9%. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Travel time prediction Non-recurrent congestion K-means classification and regression tree Neural networks

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Differentiation of honeydew honeys and blossom honeys: a new model based on colour parameters

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JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE 2019年第5期56卷 2771-2777页

作者： Bergamo, Greici Seraglio, Siluana Katia Tischer Gonzaga, Luciano Valdemiro Fett, Roseane de Mello Castanho Amboni, Renata Dias Dias, Carolinne Odebrecht Oliveira Costa, Ana Carolina Univ Fed Santa Catarina Dept Food Sci & Technol Florianopolis SC Brazil

The present study aimed to differentiate Mimosa scabrella Bentham (bracatinga) honeydew honeys from blossom honeys, with and without addition of heat treatment, and bracatinga honeydew honeys adulterated with blossom honeys (5, 15 and 25% of blossom honeys), using chromatic characterization associated with chemometric analysis. Bracatinga honeydew honeys presented unusual chromatic characteristics which allowed differentiation of blossom honeys by principal components analysis. Additionally, a classification model was developed in order to establish clear rules that characterize each group of honey. The proposed model correctly classified bracatinga honeydew honey and blossom honey samples, with and without heat treatment. Only two samples adulterated with 5% blossom honey were misclassified. The chromatic analysis associated with chemometric analysis showed promising perspectives for its exploitation being able to be used for screening and selection of bracatinga honeydew honey, fresh or thermally treated as well as fraud detection.

关键词： Data-mining C&RT classification and regression tree Principal component analysis Floral honey Botanical discrimination CIELAB

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Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes

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GEODERMA 2007年第3-4期142卷 285-293页

作者： Minasny, Budiman McBratney, Alex B. Univ Sydney Fac Agr Food & Nat Resources Sydney NSW 2006 Australia

Mapping soil classes digitally generally starts with soil profile description with observed soil classes at a taxonomic level in a particular classification system. At each soil observation location there is a set of co-located environmental variables, and the challenge is to correlate the soil class with the environmental variables. The current methodology treats soil classes as 'labels' and their prediction only considers the minimisation of the misclassification error. Soil classes at any taxonomic level have taxonomic relationships between each other, and in some instances the errors in prediction of certain classes are more serious than the others. No statistical procedure so far has been utilised to account for these relationships. This paper shows that in digital mapping of soil classes, we can incorporate the taxonomic distance between soil classes in a supervised classification routine. Using classification trees, we can specify an algorithm that minimises the taxonomic distance rather than misclassification error. Two examples are given in this paper for mapping soil orders in the Australian soil classification system. A site in the Edgeroi area showed the advantage of using the method that minimises the taxonomic distance. Meanwhile a site in the Hunter Valley showed minimising the misclassification error performed similarly to minimising taxonomic distance. The advantages and challenges of using soil taxonomic distance are discussed. (C) 2007 Elsevier B.V. All rights reserved.

关键词： soil classification spatial prediction decision tree supervised classification classification and regression tree

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A COMPARATIVE STUDY OF DATA MINING METHODS IN CONSUMER LOANS CREDIT SCORING MANAGEMENT

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Journal of Systems Science and Systems Engineering 2006年第4期15卷 419-435页

作者： Wenbing XIAO Qian ZHAO Qi FEI Institute of Systems Engineering Huazhong University of Science & Technology Wuhan 430074 China School of Economics Renmin University of China Beijing 100872 China

Credit scoring has become a critical and challenging management science issue as the credit industry has been facing stiffer competition in recent years. Many classification methods have been suggested to tackle this problem in the literature. In this paper, we investigate the performance of various credit scoring models and the corresponding credit risk cost for three real-life credit scoring data sets. Besides the well-known classification algorithms （e.g. linear discriminant analysis, logistic regression, neural networks and k-nearest neighbor）, we also investigate the suitability and performance of some recently proposed, advanced data mining techniques such as support vector machines （SVMs）, classification and regression tree （CART）, and multivariate adaptive regression splines （MARS）. The performance is assessed by using the classification accuracy and cost of credit scoring errors. The experiment results show that SVM, MARS, logistic regression and neural networks yield a very good performance. However, CART and MARS＇s explanatory capability outperforms the other methods.

关键词： Data mining credit scoring classification and regression tree support vector machines,multivariate adaptive regression splines credit-risk evaluation

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