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Simplification Methods for Model Trees with Regression and Splitting Nodes [chapter]

Michelangelo Ceci, Annalisa Apice, Donato Malerba
Machine Learning and Data Mining in Pattern Recognition  
In this paper the problem of simplifying model trees with both regression and splitting nodes is faced.  ...  Its main characteristic is the construction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space.  ...  The work presented in this paper is in partial fulfillment of the research objectives set by the MIUR COFIN-2001 project on "Methods for the extraction, validation and representation of statistical information  ... 
doi:10.1007/3-540-45065-3_3 dblp:conf/mldm/CeciAM03 fatcat:2agailf3h5drzbw5fl2s2alrye

Mining Model Trees from Spatial Data [chapter]

Donato Malerba, Michelangelo Ceci, Annalisa Appice
2005 Lecture Notes in Computer Science  
We propose a method, namely Mrs-SMOTI, that takes advantage from a tight-integration with spatial databases and mines regression models in form of trees in order to partition the sample space.  ...  Mining regression models from spatial data is a fundamental task in Spatial Data Mining.  ...  In model trees, global effects can be represented by An example of spatial model tree with regression and splitting nodes.  ... 
doi:10.1007/11564126_20 fatcat:w7ogbh4rovgijecwsb5dpc54fu

Top-down induction of model trees with regression and splitting nodes

D. Malerba, F. Esposito, M. Ceci, A. Appice
2004 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Its main characteristic is the induction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space.  ...  Model trees are an extension of regression trees that associate leaves with multiple regression models.  ...  Therefore, a third research direction is the a posteriori simplification of model trees with both regression nodes and splitting nodes.  ... 
doi:10.1109/tpami.2004.1273937 pmid:15460282 fatcat:5i3gr7b5bbh6zi4tdknan5yehq

Mining Model Trees: A Multi-relational Approach [chapter]

Annalisa Apice, Michelangelo Ceci, Donato Malerba
2003 Lecture Notes in Computer Science  
A model tree is a tree-structured prediction model whose leaves are associated with multiple linear regression models.  ...  , and split nodes, which perform tests on attributes or the join condition and eventually partition the training set.  ...  The authors thank Hendrik Blockeel for providing mutagenesis and biodegradability datasets.  ... 
doi:10.1007/978-3-540-39917-9_3 fatcat:lkxci2ajpbhezlo4ccmwnohufu

Review Paper on Decision Tree Data Mining Algorithms to Improve Accuracy in Identifying Classified Instances using Large Dataset

Gurpreet Singh et al., Gurpreet Singh et al.,
2017 International Journal of Computer Science Engineering and Information Technology Research  
The CART distance based algorithm with the classification tree paradigm based on the C45 algorithm.  ...  Then the incorrectly classified instances are duplicated with the previous data set and finally C45 is applied to complete the classification procedure of biomedical data.  ...  INTRODUCTION Decision trees basically us the hierarchal model of decisions and their consequences. The structure of decision tree includes branch, root node and leaf node.  ... 
doi:10.24247/ijcseitraug20179 fatcat:pnt2z4yavjhgdpv5gfsentjhs4

A privacy protection technique for publishing data mining models and research data

Yu Fu, Zhiyuan Chen, Gunes Koru, Aryya Gangopadhyay
2010 ACM Transactions on Management Information Systems  
Research data often needs to be published along with the data mining model for verification or reanalysis.  ...  This article proposes a technique that not only protects privacy, but also guarantees that the same model, in the form of decision trees or regression trees, can be built from the sanitized data.  ...  Experimental results show that our approach not only preserves decision tree and regression tree models, but also leads to better mining quality for several popular mining methods over the sanitized data  ... 
doi:10.1145/1877725.1877732 fatcat:socm3qximvglrd5pq5fdwqz6pq

Mining Tolerance Regions with Model Trees [chapter]

Annalisa Appice, Michelangelo Ceci
2006 Lecture Notes in Computer Science  
In particular, we consider model trees mined by SMOTI (Stepwise Model Tree Induction) that is a system for data-driven stepwise construction of model trees with regression and splitting nodes and we extend  ...  This problem, known as regression, requires that samples of past experience with known continuous answers are examined and generalized in a regression model to be used in predicting future examples.  ...  Acknowledgment This work is partial fulfillment of the research objective of COFIN-2005 project "Metodi di Data Mining Spaziali si supporto alle decisioni ambientali".  ... 
doi:10.1007/11875604_63 fatcat:2lfj5ku2g5a5tdnefmq74i6mgi

Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And IBM Intelligent Miner A Comparative Analysis

Abdullah M. Al Ghoson
2011 International Journal of Management & Information Systems  
Decision tree induction and Clustering are two of the most prevalent data mining techniques used separately or together in many business applications.  ...  This paper aims to provide a comparative analysis for three popular data mining software tools, which are SAS® Enterprise Miner, SPSS Clementine, and IBM DB2® Intelligent Miner based on four main criteria  ...  CART provides binary split classification and regression tree algorithms that split both categorical and interval target variables.  ... 
doi:10.19030/ijmis.v14i3.841 fatcat:r3ynp27de5blnog66ysalwksd4

Cluster-Based Logistic Regression Model for Holiday Travel Mode Choice

Juan Li, Jinxian Weng, Chunfu Shao, Hongwei Guo
2016 Procedia Engineering  
At first, a regression and classification tree approach is employed to split the source date to clusters.  ...  Based on the data collected from the Beijing Fragrant Hills Park during the Qingming Festival (Tomb-Sweeping Day), an optimal tree with two levels and three leaf nodes is built and the collected data are  ...  When the data enter the root node of the tree, all candidate splits among all variables are searched by a test with a splitting criterion, which is a central problem in the construction of the trees.  ... 
doi:10.1016/j.proeng.2016.01.310 fatcat:hady352qinabtf6s5ety64mrpu

Analysis of Various Decision Tree Algorithms for Classification in Data Mining

Bhumika Gupta, Aditya Rawat, Akshay Jain, Arpit Arora, Naresh Dhami
2017 International Journal of Computer Applications  
This process is known as Data Mining. [4] .Among the various data mining techniques, Decision Tree is also the popular one.  ...  A decision tree is a flow chart-like structure in which each internal node represents a "test" on an attribute where each branch represents the outcome of the test and each leaf node represents a class  ...  Gini Index is used as selecting the splitting attribute. The CART is also used for regression analysis with the help of regression tree.  ... 
doi:10.5120/ijca2017913660 fatcat:z4gnmvfm7vav5cacxwhwcxk4ha

Assessing Residual Value of Heavy Construction Equipment Using Predictive Data Mining Model

Hongqin Fan, Simaan AbouRizk, Hyoungkwan Kim, Osmar Zaïane
2008 Journal of computing in civil engineering  
This paper introduces a data mining based approach for estimating the residual value of heavy construction equipment using a predictive data mining model, and its potential benefits on the decision making  ...  factors, and such rules or models are difficult to integrate into a decision support system.  ...  Acknowledgments This work was supported by the Natural Sciences and Engineering Research Council of Canada ͑Grant No. CRD 226956-99͒ and Yonsei Research Grant.  ... 
doi:10.1061/(asce)0887-3801(2008)22:3(181) fatcat:zlzvcmt5craxreloacqim3nkzy

Educational Data Mining by Using Neural Network

Nitya Upadhyay
2016 International Journal of Computer Applications Technology and Research  
The ID3, C4.5 and CART decision tree algorithms are already implemented on the data of students to anticipate their accomplishment.  ...  Decision tree provides the more correct and relevant results which can be beneficial in improvement of learning outcomes of a student.  ...  As the tree is grown and nodes are split to create new children, the attribute lists belonging to each node are partitioned and associated with the children.  ... 
doi:10.7753/ijcatr0502.1013 fatcat:hkyjbwuvwvasnjqqrcnnuamzly

DecisionTree for Classification and Regression: A State-of-the Art Review

Monalisa Jena, Satchidananda Dehuri
2020 Informatica (Ljubljana, Tiskana izd.)  
Classification and regression are defined under the umbrella of the prediction task of data mining.  ...  , efficient, and its performance is competitive with others in a few cases.  ...  We also aim to explore new techniques in the field of decision tree-based hierarchical multi-label classification, multi-output, and multi-objective regression trees, etc.  ... 
doi:10.31449/inf.v44i4.3023 fatcat:pjulqf3zbzd3hklq2xfyawsdce

A Survey on Decision Tree Algorithms of Classification in Data Mining

2016 International Journal of Science and Research (IJSR)  
A decision tree is a structure that includes a root node, branches, and leaf nodes.  ...  Decision tree classification technique is one of the most popular data mining techniques. In decision tree divide and conquer technique is used as basic learning strategy.  ...  It builds both classifications and regression trees. The classification tree construction by CART is based on binary splitting of the attributes.  ... 
doi:10.21275/v5i4.nov162954 fatcat:mkuvdvijtbavpck6taiyesg3ky

Applying Data Mining Techniques for Predicting Diseases

Basant Ali Sayed, Mona Nasr
2019 International journal of advanced networking and applications  
Nowadays, data mining is getting used in a vast area .The Nature of the medical field is made with the knowledge wherever there's a spread of data but untapped during a correct. and thus, the foremost  ...  The various techniques of data mining are used and compared during this analysis.  ...  REP Tree could be a quick decision tree learner that builds decision/regression tree exploitation information gain as the splitting criterion, and prunes it exploitation reduced error pruning.  ... 
doi:10.35444/ijana.2019.11027 fatcat:csukqxn4prfflhsjvzfcbvzmiu
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