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Evolutionary Algorithms in Decision Tree Induction [chapter]

Francesco Mola, Raffaele Miele, Claudio Conversano
2008 Advances in Evolutionary Algorithms  
Decision tree induction Decision Tree Induction (DTI) is a tool to induce a classification or regression model from (usually large) datasets characterized by N observations (records), each one containing  ...  Actually, two kinds of model can be estimated using decision trees: classification trees if y is nominal, and regression trees if y is numerical.  ...  In our case, the greedy tree induction rule solution quality is used.  ... 
doi:10.5772/6117 fatcat:4k577srk3vfv5ncl7xisv7ptm4

Lookahead and Pathology in Decision Tree Induction

Sreerama K. Murthy, Steven Salzberg
1995 International Joint Conference on Artificial Intelligence  
The standard approach t decision tree in duction is a top-down greedy agonthm that makes locall} optimal irrevocable decisions at each node of a tree In this paper we empir-•call} study an alternative  ...  trees induced by the greedv approach to that of trees induced using lookahead The main observations from our experments are (1) the greedv approach consistently produced trees that were just as at curate  ...  game tree Our discovery that deeper search can lead to inferior decision tree 5 ; thus extends the earlier pathology results to a new domain It is possible that pathology is a side-effect of the way heuristic  ... 
dblp:conf/ijcai/MurthyS95 fatcat:nrmo5hlmyzhevpiez64s3bs7xi

Evolutionary Algorithm for Decision Tree Induction [chapter]

Dariusz Jankowski, Konrad Jackowski
2014 Lecture Notes in Computer Science  
by the European Social Fund and the state budget of the Czech Republic.  ...  This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology dedicated for Young Scientists (grant no.  ...  In this work, we propose use of the evolutionary algorithms (EAs) paradigm as an alternate heuristic to generate model trees. EAs has been successfully applied to decision tree induction, e.g.  ... 
doi:10.1007/978-3-662-45237-0_4 fatcat:vsqqnkgrszg4tcw6fachj2vxq4

k-Anonymous Decision Tree Induction [chapter]

Arik Friedman, Assaf Schuster, Ran Wolff
2006 Lecture Notes in Computer Science  
Experiments show that embedding anonymization within the decision tree induction process provides better accuracy than anonymizing the data first and inducing the tree later.  ...  Using these definitions, we present decision tree induction algorithms that are guaranteed to maintain k-anonymity of the learning examples.  ...  Section 3 demonstrates how the definitions can be incorporated within decision tree induction algorithms to guarantee k-anonymous output.  ... 
doi:10.1007/11871637_18 fatcat:2rhg67qlzvcizcqe226fmg5czm

Constructive Induction On Decision Trees

Christopher J. Matheus, Larry A. Rendell
1989 International Joint Conference on Artificial Intelligence  
CITRE performs feature construction using decision trees and simple domain knowledge as constructive biases.  ...  Selective induction techniques perform poorly when the features are inappropriate for the target concept.  ...  Acknowledgment We would like to thank Gunnar Blix, Gregg Gunsch, Carl Kadie, Doug Medin, and David Wilkins for their helpful discussion and comments on the issues developed in this paper.  ... 
dblp:conf/ijcai/MatheusR89 fatcat:ymhz7suvlvgkzl5yydrinofclq

Decision Tree Induction Systems: A Bayesian Analysis [article]

Wray L. Buntine
2013 arXiv   pre-print
perform a greedy search of the space of decision rules to find one in which there is strong posterior belief.  ...  Decision tree induction systems are being used for knowledge acquisition in noisy domains.  ...  Ross Quinlan's many research initiatives provided the incentive. Thanks are also due to Peter Cheeseman for indicating references and the workshop program committee for changes suggested.  ... 
arXiv:1304.2732v1 fatcat:h77ayiwngbc4xoij45nw7vzsdm

Different decision tree induction strategies for a medical decision problem

Robert Burduk, Michal Wozniak
2012 Open Medicine  
We consider if it is worth using expert knowledge and learning set at the same time. The article shows two groups of decision tree approaches to the problem under consideration.  ...  The second approach utilizes expert knowledge for specifying the decision tree structure and learning set for determining mode of decision making in each node based on Bayes decision theory.  ...  Acknowledgments This work is supported by The Polish National Science Center under the grant which is realizing in years 2011-2014.  ... 
doi:10.2478/s11536-011-0142-x fatcat:woro6fayrrcupe5vkhihitqzwm

Scaling up Heuristic Planning with Relational Decision Trees

T. De la Rosa, S. Jimenez, R. Fuentetaja, D. Borrajo
2011 The Journal of Artificial Intelligence Research  
This paper shows two methods for guiding the search of a heuristic planner with the learned classifiers. The first one consists of using the resulting classifier as an action policy.  ...  Experiments over a variety of domains reveal that our heuristic planner using the learned classifiers solves larger problems than state-of-the-art planners.  ...  Acknowledgments This work has been partially supported by the Spanish MICIIN project TIN2008-06701-C03-03 and the regional CAM-UC3M project CCG08-UC3M/TIC-4141.  ... 
doi:10.1613/jair.3231 fatcat:mhei6rvzebgnjnfazakhwf3vfi

Lookahead-based algorithms for anytime induction of decision trees

Saher Esmeir, Shaul Markovitch
2004 Twenty-first international conference on Machine learning - ICML '04  
The majority of the existing algorithms for learning decision trees are greedy-a tree is induced top-down, making locally optimal decisions at each node.  ...  Furthermore, the greedy algorithms require a fixed amount of time and are not able to generate a better tree if additional time is available.  ...  The algorithm exhibits good anytime behavior and serves as a contract algorithm for producing better decision trees with additional resource allocation.  ... 
doi:10.1145/1015330.1015373 dblp:conf/icml/EsmeirM04 fatcat:r2tsviog6faqbixe7jhnp3plz4

Reusable components in decision tree induction algorithms

Milija Suknovic, Boris Delibasic, Milos Jovanovic, Milan Vukicevic, Dragana Becejski-Vujaklija, Zoran Obradovic
2011 Computational statistics (Zeitschrift)  
The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE  ...  Combining reusable components allows the replication of original algorithms, their modification but also the creation of new decision tree induction algorithms.  ...  Acknowledgments This research was supported by a grant from the Serbian Ministry of Science, TR 12013.  ... 
doi:10.1007/s00180-011-0242-8 fatcat:37piziekzvgx3deag4vd6rgsby

Improved Information Gain Estimates for Decision Tree Induction [article]

Sebastian Nowozin
2012 arXiv   pre-print
During induction of decision trees one aims to find predicates that are maximally informative about the prediction target.  ...  In effect our modifications yield improved predictive performance and are simple to implement in any decision tree code.  ...  The author thanks Jamie Shotton, Antonio Criminisi, and the anonymous reviewers for their helpful feedback.  ... 
arXiv:1206.4620v1 fatcat:x6wabv5g6fd6vapr7w7ohyzb3e

Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm [article]

P. D. Turney
1995 arXiv   pre-print
ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm.  ...  The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification  ...  This work was presented in informal talks at the University of Ottawa and the Naval Research Laboratory. Thanks to both audiences for their feedback.  ... 
arXiv:cs/9503102v1 fatcat:m6cucwf5szcnxkc3c74cvyklhq

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Rodrigo Coelho Barros, Márcio Porto Basgalupp, André C. P. L. F. de Carvalho, Alex A. Freitas
2012 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divideand-conquer approach.  ...  Finally, a number of references is provided that describe applications of evolutionary algorithms for decision tree induction in different domains.  ...  ACKNOWLEDGMENT The authors would like to thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de  ... 
doi:10.1109/tsmcc.2011.2157494 fatcat:wo3ak7qiwrcz5fosspwjgstjfi

Clus-DTI: improving decision-tree classification with a clustering-based decision-tree induction algorithm

Rodrigo C. Barros, Márcio P. Basgalupp, André C. P. L. F. de Carvalho, Marcos G. Quiles
2012 Journal of the Brazilian Computer Society  
Most decision-tree induction algorithms rely on a suboptimal greedy top-down recursive strategy for growing the tree.  ...  Our intention is to investigate how clustering data as a part of the induction process affects the accuracy and complexity of the generated models.  ...  The main disadvantages of evolutionary induction of decision trees are related to time and space constraints. EAs are a solid but computational expensive heuristic.  ... 
doi:10.1007/s13173-012-0075-5 fatcat:47erso2hgvdxrhdpnz6nbpto4e

A further comparison of splitting rules for decision-tree induction

Wray Buntine, Tim Niblett
1992 Machine Learning  
One approach to learning classification rules from examples is to build decision trees.  ...  A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tree building, which uses a "splitting rule" to grow trees with a greedy recursive partitioning algorithm.  ...  In the area of decision tree induction empirical comparisons have been widely used by Breiman, et al.  ... 
doi:10.1007/bf00994006 fatcat:uff5m7hgdvhizgd74qx3gtvm6q
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