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Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Basgalupp, Márcio P.; Barros, Rodrigo Coelho; Carvalho, André Carlos Ponce de Leon Ferreira de; Freitas, Alex A.
Fonte: Elsevier; New York Publicador: Elsevier; New York
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
56.24%
Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.; Fundação de Amparo à Pesquisa do Estado de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico

A framework for bottom-up induction of oblique decision trees

Barros, Rodrigo C.; Jaskowiak, Pablo A.; Cerri, Ricardo; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Elsevier; Amsterdam Publicador: Elsevier; Amsterdam
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.41%
Decision-tree induction algorithms are widely used in knowledge discovery and data mining, specially in scenarios where model comprehensibility is desired. A variation of the traditional univariate approach is the so-called oblique decision tree, which allows multivariate tests in its non-terminal nodes. Oblique decision trees can model decision boundaries that are oblique to the attribute axes, whereas univariate trees can only perform axis-parallel splits. The vast majority of the oblique and univariate decision-tree induction algorithms employ a top-down strategy for growing the tree, relying on an impurity-based measure for splitting nodes. In this paper, we propose BUTIF—a novel Bottom-Up Oblique Decision-Tree Induction Framework. BUTIF does not rely on an impurity-measure for dividing nodes, since the data resulting from each split is known a priori. For generating the initial leaves of the tree and the splitting hyperplanes in its internal nodes, BUTIF allows the adoption of distinct clustering algorithms and binary classifiers, respectively. It is also capable of performing embedded feature selection, which may reduce the number of features in each hyperplane, thus improving model comprehension. Different from virtually every top-down decision-tree induction algorithm...

LEGAL-Tree: um algoritmo genético multi-objetivo para indução de árvores de decisão; LEGAL-Tree: a lexocographic genetic algorithm for learning decision trees

Basgalupp, Márcio Porto
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 23/02/2010 PT
Relevância na Pesquisa
66.49%
Dentre as diversas tarefas em que os algoritmos evolutivos têm sido empregados, a indução de regras e de árvores de decisão tem se mostrado uma abordagem bastante atrativa em diversos domínios de aplicação. Algoritmos de indução de árvores de decisão representam uma das técnicas mais populares em problemas de classificação. Entretanto, os algoritmos tradicionais de indução apresentam algumas limitações, pois, geralmente, usam uma estratégia gulosa, top down e com particionamento recursivo para a construção das árvores. Esses fatores degradam a qualidade dos dados, os quais podem gerar regras estatisticamente não significativas. Este trabalho propõe o algoritmo LEGAL-Tree, uma nova abordagem baseada em algoritmos genéticos para indução de árvores de decisão. O algoritmo proposto visa evitar a estratégia gulosa e a convergência para ótimos locais. Para isso, esse algoritmo adota uma abordagem multi-objetiva lexicográfica. Nos experimentos realizados sobre bases de dados de diversos problemas de classificação, a função de fitness de LEGAL-Tree considera as duas medidas mais comuns para avaliação das árvores de decisão: acurácia e tamanho da árvore. Os resultados obtidos mostraram que LEGAL-Tree teve um desempenho equivalente ao algoritmo SimpleCart (implementação em Java do algoritmo CART) e superou o tradicional algoritmo J48 (implementação em Java do algoritmo C4.5)...

Uma abordagem para a indução de árvores de decisão voltada para dados de expressão gênica; An Approach for the Induction of Decision Trees Focused on Gene Expression Data

Perez, Pedro Santoro
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 18/04/2012 PT
Relevância na Pesquisa
66.36%
Estudos de expressão gênica têm sido de extrema importância, permitindo desenvolver terapias, exames diagnósticos, medicamentos e desvendar uma infinidade de processos biológicos. No entanto, estes estudos envolvem uma série de dificuldades: grande quantidade de genes, sendo que geralmente apenas um pequeno número deles está envolvido no problema estudado; presença de ruído nos dados analisados; entre muitas outras. O projeto de pesquisa deste mestrado consiste no estudo de algoritmos de indução de árvores de decisão; na definição de uma metodologia capaz de tratar dados de expressão gênica usando árvores de decisão; e na implementação da metodologia proposta como algoritmos capazes de extrair conhecimento a partir desse tipo de dados. A indução de árvores de decisão procura por características relevantes nos dados que permitam modelar precisamente um conceito, mas tem também a preocupação com a compreensibilidade do modelo gerado, auxiliando os especialistas na descoberta de conhecimento, algo importante nas áreas médica e biológica. Por outro lado, tais indutores apresentam relativa instabilidade, podendo gerar modelos bem diferentes com pequenas mudanças nos dados de treinamento. Este é um dos problemas tratados neste mestrado. Mas o principal problema tratado se refere ao comportamento destes indutores em dados de alta dimensionalidade...

On the automatic design of decision-tree induction algorithms; Sobre o projeto automático de algoritmos de indução de árvores de decisão

Barros, Rodrigo Coelho
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 06/12/2013 EN
Relevância na Pesquisa
56.26%
Decision-tree induction is one of the most employed methods to extract knowledge from data. There are several distinct strategies for inducing decision trees from data, each one presenting advantages and disadvantages according to its corresponding inductive bias. These strategies have been continuously improved by researchers over the last 40 years. This thesis, following recent breakthroughs in the automatic design of machine learning algorithms, proposes to automatically generate decision-tree induction algorithms. Our proposed approach, namely HEAD-DT, is based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes. HEAD-DT works over several manually-designed decision-tree components and combines the most suitable components for the task at hand. It can operate according to two different frameworks: i) evolving algorithms tailored to one single data set (specific framework); and ii) evolving algorithms from multiple data sets (general framework). The specific framework aims at generating one decision-tree algorithm per data set, so the resulting algorithm does not need to generalise beyond its target data set. The general framework has a more ambitious goal, which is to generate a single decision-tree algorithm capable of being effectively applied to several data sets. The specific framework is tested over 20 UCI data sets...

Wavelets and decision trees for target detection over sea surface using cosmo-skymed SAR data

Paes, Rafael L.; Pagamisse, Aylton
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 582-589
ENG
Relevância na Pesquisa
66.44%
We are investigating the combination of wavelets and decision trees to detect ships and other maritime surveillance targets from medium resolution SAR images. Wavelets have inherent advantages to extract image descriptors while decision trees are able to handle different data sources. In addition, our work aims to consider oceanic features such as ship wakes and ocean spills. In this incipient work, Haar and Cohen-Daubechies-Feauveau 9/7 wavelets obtain detailed descriptors from targets and ocean features and are inserted with other statistical parameters and wavelets into an oblique decision tree. © 2011 Springer-Verlag.

Automatic classification of location contexts with decision trees

Santos, Maribel Yasmina; Moreira, Adriano
Fonte: Universidade do Minho. Escola de Engenharia Publicador: Universidade do Minho. Escola de Engenharia
Tipo: Conferência ou Objeto de Conferência
Publicado em //2006 ENG
Relevância na Pesquisa
56.14%
Location contexts are geographic regions, with well defined boundaries, that can be used to characterize the context of the persons lying inside them. In this paper we describe a process that exploits the increasing availability of geographic data to automatically create and classify location contexts. The pro-posed process generates new geographic regions from a database of Points Of Interest through the use of spatial clustering techniques, and classifies them automatically using a decision tree based method. Some preliminary results demonstrate the validity of this approach, while suggesting that a richer geographic database could produce location contexts of higher quality.; Fundação para a Ciência e a Tecnologia (FCT).

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis, neural networks and decision trees

Ginoris, Y. P.; Amaral, A. L.; Nicolau, Ana; Coelho, M. A. Z.; Ferreira, E. C.
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Publicado em /07/2007 ENG
Relevância na Pesquisa
66.31%
Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. Thiswork presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes...

Water Quality Modelling using Artificial Neural Networks and Decision Trees

Couto, Catarina; Vicente, Henrique; Neves, José
Fonte: Institute of Water Management, Hydrology and Hydraulic Engineering – University of Natural Resources and Life Sciences – Vienna Publicador: Institute of Water Management, Hydrology and Hydraulic Engineering – University of Natural Resources and Life Sciences – Vienna
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.22%
The water quality at ground zero in a given region largely depends on the nature and the extent of the industrial, agricultural and other anthropogenic activities in the catchments. Undeniably, ensuring an efficient water management system is a major goal in contemporary societies, taking into account its importance to the living organisms health and the need to safeguard and to promote its sustainable use. However, the assessment of the data quality of a dam`s water is being done through analytical methods, which may be not a good way of such an accomplishment, due to the distances to be covered, the number of parameters to be considered and the financial resources that will be spent. Under these circumstances, the modelling of water quality in reservoirs is essential in the resolution of environmental problems, and has lately been asserting itself as a relevant tool for a sustainable and harmonious progress of the populations. This work describes the training, validation and application of Artificial Neural Networks (ANNs) and Decision Trees (DTs) to forecast the water quality of the Odivelas reservoir, in the south region of Portugal, over a period of 10 (ten) years. Two different strategies were followed to build predictive models for water quality. One of them used chemical parameters data (strategy A) while the other one used hydrometric and meteorological data (strategy B). In terms of the former strategy...

Extracting decision rules from police accident reports through decision trees

O??a, Juan de; L??pez Maldonado, Griselda; Abell??n, Joaqu??n
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.33%
Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand...

Using decision trees to extract decision rules from police reports on road accidents

L??pez Maldonado, Griselda; O??a, Juan de; Abell??n, Joaqu??n
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
66.31%
The World Health Organization (WHO) considers that traffic accidents are major public health problem worldwide, for this reason safety managers try to identify the main factors affecting the severity as consequence of road accidents. In order to identify these factors, in this paper, Data Mining (DM) techniques such as Decision Trees (DTs), have been used. A dataset of traffic accidents on rural roads in the province of Granada (Spain) have been analyzed. DTs allow certain decision rules to be extracted. These rules could be used in future road safety campaigns and would enable managers to implement certain priority actions.

Probabilistic Decision Trees using SVM for Multi-class Classification

URIBE, Juan Sebastian; MECHBAL, Nazih; REBILLAT, Marc; BOUAMAMA, Karima; PENGOV, Marco
Fonte: IEEE Publicador: IEEE
EN_US
Relevância na Pesquisa
56.21%
In the automotive repairing backdrop, retrieving from previously solved incident the database features that could support and speed up the diagnostic is of great usefulness. This decision helping process should give a fixed number of the more relevant diagnostic classified in a likelihood sense. It is a probabilistic multi-class classification problem. This paper describes an original classification technique, the Probabilistic Decision Tree (PDT) producing a posteriori probabilities in a multi-class context. It is based on a Binary Decision Tree (BDT) with Probabilistic Support Vector Machine classifier (PSVM). At each node of the tree, a bi-class SVM along with a sigmoid function are trained to give a probabilistic classification output. For each branch, the outputs of all the nodes composing the branch are combined to lead to a complete evaluation of the probability when reaching the final leaf (representing the class associated to the branch). To illustrate the effectiveness of PDTs, they are tested on benchmark datasets and results are compared with other existing approaches.; This research has been sponsored by PSA.

Pattern Trees

Huang, Ziheng; Gedeon, Tamas (Tom)
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
56.41%
This paper proposes a new type of tree termed pattern trees. Like decision trees, pattern trees are an effective tool for classification applications. This paper discusses the difference between decision trees and pattern trees, and also shows that the su

Pattern Trees: An effective Machine Learning Approach

Huang, Zhiheng; Nikravesh, Masoud; Gedeon, Tamas (Tom); Azvine, Ben
Fonte: Springer Publicador: Springer
Tipo: Parte de Livro
Relevância na Pesquisa
56.2%
Fuzzy classification is one of the most important applications of fuzzy logic. Its goal is to find a set of fuzzy rules which describe classification problems. Most of the existing fuzzy rule induction methods (e.g., the fuzzy decision trees induction met

Weighted Pattern Trees: A Case Study with Customer Satisfaction Dataset

Huang, Zhiheng; Nikravesh, Masoud; Azvine, Ben; Gedeon, Tamas (Tom)
Fonte: Springer Publicador: Springer
Tipo: Conference paper
Relevância na Pesquisa
56.2%
A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, patt

A New Pruning Method for Solving Decision Trees and Game Trees

Shenoy, Prakash P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/02/2013
Relevância na Pesquisa
46.49%
The main goal of this paper is to describe a new pruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node. Instead, we require a joint probability for each path from the root node to a leaf node. We compare the pruning method to the traditional rollback method for decision trees and game trees. For problems that require Bayesian revision of probabilities, a scenario tree representation with the pruning method is more efficient than a decision tree representation with the rollback method. For game trees, the pruning method is more efficient than the rollback method.; Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)

On Cascading small decision trees

Minguillón, Julià
Fonte: Bellaterra : Universitat Autònoma de Barcelona, Publicador: Bellaterra : Universitat Autònoma de Barcelona,
Tipo: Tesis i dissertacions electròniques; info:eu-repo/semantics/doctoralThesis Formato: application/pdf
Publicado em //2003 ENG; ENG
Relevância na Pesquisa
46.54%
Consultable des del TDX; Títol obtingut de la portada digitalitzada; Aquesta tesi tracta sobre la utilització d'arbres de decisió petits per a la classificació i la mineria de dades. La idea intuïtiva darrera d'aquesta tesi és que una seqüència d'arbres de decisió petits pot rendir millor que un arbre de decisió gran, reduint tan el cost d'entrenament com el d'explotació. El nostre primer objectiu va ser desenvolupar un sistema capaç de reconèixer diferents tipus d'elements presents en un document com ara el fons, text, línies horitzontals i verticals, dibuixos esquemàtics i imatges. Aleshores, cada element pot ser tractat d'acord a les seves característiques. Per exemple, el fons s'elimina i no és processat, mentre que les altres regions serien comprimides usant l'algorisme apropiat, JPEG amb pèrdua per a les imatges i un mètode sense pèrdua per a la resta, per exemple. Els primers experiments usant arbres de decisió varen mostrar que els arbres de decisió construïts eren massa grans i que patien de sobre-entrenament. Aleshores, vàrem tractar d'aprofitar la redundància espacial present en les imatges, utilitzant una aproximació de resolució múltiple: si un bloc gran no pot ser correctament classificat, trencar-lo en quatre sub-blocs i repetir el procés recursivament per a cada sub-bloc...

Predicate selection for structural decision trees

Ng, Kee Siong; Lloyd, John
Fonte: Springer Publicador: Springer
Tipo: Conference paper
Relevância na Pesquisa
66.26%
We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in classification learning, where we reconsider the use of accuracy as a predicate selection function and show that, on practical grounds, it is a better alternative to other commonly used functions. The second is in regression learning, where we consider the standard mean squared error measure and give a predicate pruning result for it.

Automated Classification of Bitmap Images using Decision Trees

Surynek,Pavel; Luksová,Ivana
Fonte: Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo Publicador: Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2011 EN
Relevância na Pesquisa
56.14%
The paper addresses the design of a method for automated classification of bitmap images into classes described by the user in natural language. Examples of such naturally defined classes are images depicting buildings, landscape, artistic images, etc. The proposed classification method is based on the extraction of suitable attributes from a bitmap image such as contrast, histogram, the occurrence of straight lines, etc. Extracted attributes are subsequently processed by a decision tree which has been trained in advance. A performed experimental evaluation with 5 classification classes showed that the proposed method has the accuracy of 75%-85%. The design of the method is general enough to allow the extension of the set of classification classes as well as the number of extracted attributes to increase the accuracy of classification.

Decision Tree based Classifiers for Large Datasets

Franco-Arcega,Anilu; Carrasco-Ochoa,Jesús Ariel; Sánchez-Díaz,Guillermo; Martínez-Trinidad,José Francisco
Fonte: Centro de Investigación en computación, IPN Publicador: Centro de Investigación en computación, IPN
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2013 EN
Relevância na Pesquisa
56.26%
In this paper, several algorithms have been developed for building decision trees from large datasets. These algorithms overcome some restrictions of the most recent algorithms in the state of the art. Three of these algorithms have been designed to process datasets described exclusively by numeric attributes, and the fourth one, for processing mixed datasets. The proposed algorithms process all the training instances without storing the whole dataset in the main memory. Besides, the developed algorithms are faster than the most recent algorithms for building decision trees from large datasets, and reach competitive accuracy rates.