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Machine Learning Aided Efficient Tools for Risk Evaluation and Operational Planning of Multiple Contingencies [chapter]

Venkat Krishnan
2014 Studies in Computational Intelligence  
Utilizing realistic probability distributions of operating conditions together with machine learning techniques makes the risk assessment process of multiple contingencies credible and computationally  ...  Generally, planning is done for the most critical contingency, with the assumption that the solution strategy for the most constraining contingency will also perform well on the contingencies that have  ...  This is because it uses linear sensitivities computed for multiple stress directions, in conjunction with machine learning methods to estimate contingency severity.  ... 
doi:10.1007/978-3-319-13132-0_12 fatcat:4pyyeia7qvhe5h2i44ust7uw2i

Predictive analytics of insurance claims using multivariate decision trees

Zhiyu Quan, Emiliano A. Valdez
2018 Dependence Modeling  
In addition, the extension of using decision trees with multivariate response variables started to develop and it is the purpose of this paper to apply multivariate tree models to insurance claims data  ...  with correlated responses.  ...  The data used in this paper was provided by Gee Lee and Edward W. (Jed) Frees of the University of Wisconsin in Madison; we extend our appreciation to them for allowing us to use the data.  ... 
doi:10.1515/demo-2018-0022 fatcat:qbgtn2wwqjby5cycgm6qvmljcy

Predictive Analytics of Insurance Claims Using Multivariate Decision Trees

Zhiyu Quan, Emiliano A. Valdez
2018 Social Science Research Network  
In addition, the extension of using decision trees with multivariate response variables started to develop and it is the purpose of this paper to apply multivariate tree models to insurance claims data  ...  With multivariate tree models, we are able to capture the inherent relationship among the response variables and we nd that the marginal predictive model based on multivariate trees is an improvement in  ...  The data used in this paper was provided by Gee Lee and Edward W. (Jed) Frees of the University of Wisconsin in Madison; we extend our appreciation to them for allowing us to use the data.  ... 
doi:10.2139/ssrn.3216135 fatcat:ruzpxccmcnhnpnb45wblmwkzpi

Methodologies from Machine Learning in Data Analysis and Software

D. Michie
1991 Computer journal  
Computer-oriented techniques can now be used, however, in integration with established methods from classical statistics to generate rulestructured classifiers which not only make a better job of classifying  ...  New developments in the computer induction of decision rules have contributed to two areas, multivariate data analysis and computer assisted software engineering.  ...  A MORE DEMANDING DEFINITION OF LEARNING A more demanding definition of learning is now coming from applied artificial intelligence: a learning system uses sample data to generate an up-dated basis for  ... 
doi:10.1093/comjnl/34.6.559 fatcat:fw7usy456jcl3fq4th4p3fxgby

Nonparametric classification of pixels under varying outdoor illumination

Shashi Buluswar, Bruce A. Draper, David P. Casasent
1994 Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision  
Our method uses multivariate decision trees for piecewise linear approximation of the region corresponding to the object's appearance in color space.  ...  We present a technique that uses training images of an object under daylight to learn the shift in color of an object.  ...  We use multivariate decision trees to learn a volumetric approximation in RGB to the reflectance volume of an object and to classify pixels.  ... 
doi:10.1117/12.188926 fatcat:5xw3wsnn35a5pjiny6z2o3lsqu

Stock Market Prices Prediction using Random Forest and Extra Tree Regression

2019 International journal of recent technology and engineering  
Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and  ...  reliable and accurate tool to the society in the estimation of stock exchange or share market scales.  ...  Decision tree for regression uses the ID3 algorithm to build the tree by repositioning information gain with Standard Deviation Reduction (SDR).  ... 
doi:10.35940/ijrte.c4314.098319 fatcat:xxe37ysqs5as7p2yulmhonx75a

Integrating Decision Tree And Spatial Cluster Analysis For Landslide Susceptibility Zonation

Chien-Min Chu, Bor-Wen Tsai, Kang-Tsung Chang
2009 Zenodo  
The local Getis-Ord statistics were used to cluster cells with high landslide probability.  ...  This study proposed a new approach to integrate decision tree model and spatial cluster statistic for assessing landslide susceptibility spatially.  ...  Generally, statistical classification methods are used to estimate landslide susceptibility over large and complex areas [2] .  ... 
doi:10.5281/zenodo.1077482 fatcat:pobo455ijjbmnkfyvalcozyy6m

Budding Trees

Ozan Irsoy, Olcay Taner Yildiz, Ethem Alpaydin
2014 2014 22nd International Conference on Pattern Recognition  
We propose a new decision tree model, named the budding tree, where a node can be both a leaf and an internal decision node.  ...  We use a soft tree architecture and show that the tree and its parameters can be trained using gradient-descent.  ...  SOFT DECISION TREE Equation (1) defines a hard split where in a decision node, depending on g m (x), we take one of the two branches-we either go to the left or to the right.  ... 
doi:10.1109/icpr.2014.616 dblp:conf/icpr/IrsoyYA14 fatcat:fhnz7j7bvbdtjkxzjs3fprjjka

An Arbitrary Gini Index for the Redundant Feature Datasets Analysis

Rajesh Vemulakonda, Abdul Ahad, Suresh Babu Yalavarthi, Praneeth Cheraku, Nageswara Rao Puli
2017 Indian Journal of Science and Technology  
Several algorithms have been proposed to increase the accuracy, but most of them generate complex models as the size of the data is extremely large.  ...  Objective of this paper is to build a simple model to get high accuracy.  ...  Decision Tree breaks down a dataset into subset using that associated decisions tree is incrementally developed 2 . Final result is tree with decision nodes and leaf node.  ... 
doi:10.17485/ijst/2017/v10i4/110665 fatcat:np6xx36r7ndsxgr7sg2fls3tzi

CaDET: interpretable parametric conditional density estimation with decision trees and forests

Cyrus Cousins, Matteo Riondato
2019 Machine Learning  
We introduce CaDET, an algorithm for parametric Conditional Density Estimation (CDE) based on decision trees and random forests.  ...  CaDET uses the empirical cross entropy impurity criterion for tree growth, which incentivizes splits that improve predictive accuracy more than the regression criteria or estimated mean-integrated-square-error  ...  CaDET therefore generalizes two classic decision-tree models to a broad class of parametric estimation problems.  ... 
doi:10.1007/s10994-019-05820-3 fatcat:nv2z4u6lubaoxesbxpdrq6ozkm

Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways

Fei Hua, Sampsa Hautaniemi, Rayka Yokoo, Douglas A. Lauffenburger
2006 Journal of the Royal Society Interface  
We also used the resulting decision tree to identify the minimal number of perturbations needed to change pathway behaviours.  ...  Subsequently, decision tree analysis was applied, in which we used protein concentrations to predict the simulation outcomes.  ...  This work was supported by an NIGMS P50 grant to the MIT Cell Decision Processes Centre, by a gift from Entelos, by the Academy of Finland and Helsingin Sanomain 100-Vuotissäätiö.  ... 
doi:10.1098/rsif.2005.0109 pmid:16849248 pmcid:PMC1664647 fatcat:hfzpaswxefdl3ckxz3htb24naq

Finding structure in data using multivariate tree boosting

Patrick J. Miller, Gitta H. Lubke, Daniel B. McArtor, C. S. Bergeman
2016 Psychological methods  
To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called  ...  Decision tree ensembles like random forests (Strobl, Malley, and Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest  ...  Like decision ensembles for single response variables, the multivariate random forest provides critical improvements to predictive performance compared to multivariate decision trees by combining predictions  ... 
doi:10.1037/met0000087 pmid:27918183 pmcid:PMC5142230 fatcat:e6knutii3jggjlks2xhvhhf2li

Using randomized response techniques for privacy-preserving data mining

Wenliang Du, Zhijun Zhan
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data.  ...  We conduct experiments to compare the accuracy of our decision tree with the one built from the original undisguised data.  ...  The proposed randomized response techniques allow us to estimate P (E) from P * (E).  ... 
doi:10.1145/956804.956810 fatcat:snuwxmbt55atrpwghymba2bpaa

Improving the Robustness and Encoding Complexity of Behavioural Clones [chapter]

Rui Camacho, Pavel Brazdil
2001 Lecture Notes in Computer Science  
We have investigated the use of three different representations to encode the artificial controllers: univariate decision trees as induced by C4.5; multivariate decision and regression trees as induced  ...  We obtained an increase in robustness and a lower complexity of the controllers when compared with results using other models. The controllers synthesized by cart revealed to be the most robust.  ...  Thanks are due to Universidade do Porto and JNICT for the financial support during the PhD.  ... 
doi:10.1007/3-540-44795-4_4 fatcat:cbiqctrvo5dh5dt2py4v3kbozq

Using randomized response techniques for privacy-preserving data mining

Wenliang Du, Zhijun Zhan
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data.  ...  We conduct experiments to compare the accuracy of our decision tree with the one built from the original undisguised data.  ...  The proposed randomized response techniques allow us to estimate P (E) from P * (E).  ... 
doi:10.1145/956750.956810 dblp:conf/kdd/DuZ03 fatcat:c63iuc7g7rfp3itsclvz333qqm
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