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An Evolutionary Algorithm for Making Decision Graphs for Classification Problems

Shingo Mabu, Masanao Obayashi, Takashi Kuremoto
2016 Journal of Robotics, Networking and Artificial Life (JRNAL)  
In this paper, to enhance the classification ability of decision trees, a new evolutionary algorithm for creating decision graphs is proposed as a superset of decision trees, where multi-root nodes and  ...  NNs, SVMs and their extended algorithms can create distinguished decision boundaries for accurate classification, however, they are black box models, thus the reason of the classification results is difficult  ...  Conclusions This paper proposed an evolutionary algorithm for creating decision graphs for classification problems.  ... 
doi:10.2991/jrnal.2016.3.1.11 fatcat:sonv46xq45etla3dqbolqey254

Evolutionary Multi-Objective Algorithms [chapter]

Aurora Torres, Dolores Torres, Sergio Enriquez, Eunice Ponce de Len, Elva Daz
2012 Real-World Applications of Genetic Algorithms  
This area has been approached for different techniques and methods.  ...  Real-World Applications of Genetic Algorithms 54 approach and a group of experimental results, as well as some conclusions and future work.  ...  Most interesting graph drawing problems are NP-hard and their decisional versions are NP-complete (Garey and Johnson, 1983 ), but, for most of their applications, feasible solutions with an almost optimal  ... 
doi:10.5772/36230 fatcat:4llpo3hsxfck5dathhqdekbtcy

A Review Paper on Classification of Genetic Algorithms

Dr.Vipul Sharma, Ms. Shruti Singh, Mr. Lokesh Sharma
2018 Zenodo  
For the multiclass problem, the proposed strategies are based totally on the idea of selecting a gene subset to differentiate all instructions.  ...  But, it is going to be extra powerful to solve a multiclass hassle through splitting it into a set of -class problems and fixing each trouble with a respective classification machine.In application, a  ...  INTRODUCTION Genetic Programming (GP) introduced by means of Koza in 1992 is an evolutionary algorithm designed for mechanically building and evolving computer packages.  ... 
doi:10.5281/zenodo.2345239 fatcat:ao7yvf6xkvdldiony4c3swvswa

Intelligent Information Processing [Guest editor's introduction]

Wei Quan
2018 Computing in science & engineering (Print)  
It is an interdisciplinary subject in computer science, involving neural networks, fuzzy systems, evolutionary computation, chaos dynamics, classification theory, wavelet transform, artificial intelligence  ...  The study of intelligent information processing seeks to establish theories, algorithms, and systematic methods and technology for dealing with complex system information and its uncertainty.  ...  Multicriteria decision-making concerns the structuring and solving of decision and planning problems involving multiple criteria.  ... 
doi:10.1109/mcse.2018.021651334 fatcat:kh2rm26ppbguzpmk3n2gnvjnde

Discovering in Formative Knowledge using Combined Mining Approach

Aniket A.
2015 International Journal of Computer Applications  
Proposed method combine apriori algorithm with Multi-Objective Evolutionary Algorithm. It helps to improve searching for the exact products from complex data.  ...  Data mining applications involve complex data like multiple heterogeneous data sources, different user preference and create decision making activities.  ...  The Proposed method is combined Multi Objective Evolutionary Algorithm with apriori algorithm which is used for rule mining. MOEA MOEAs have been widely used for classification of complex data.  ... 
doi:10.5120/ijca2015905243 fatcat:3c5a5rng7nf6je5omjulkcnegy

Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm

Man Leung Wong, Yuan Yuan Guo
2008 Decision Support Systems  
This paper proposes a novel method for learning Bayesian networks from incomplete databases in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation  ...  The new method can also overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms.  ...  In this paper, we propose a novel method that uses EM to handle incomplete databases with missing values and applies an evolutionary algorithm to search for good Bayesian networks.  ... 
doi:10.1016/j.dss.2008.01.002 fatcat:jk5wsgyonndytjeoiqofxbssw4

Evolutionary Decision Trees And Software Metrics For Module Defects Identification

Monica Chiş
2008 Zenodo  
The aim of this paper is to present an application of evolutionary decision trees in software engineering in order to classify the software modules that have or have not one or more reported defects.  ...  For this some metrics are used for detecting the class of modules with defects or without defects.  ...  The paper present an application of evolutionary decision trees in software engineering for reporting modules with defects.  ... 
doi:10.5281/zenodo.1071894 fatcat:hik4ksshh5bkdbtp4xufd6xe7u

Evolutionary Algorithms in Decision Tree Induction [chapter]

Francesco Mola, Raffaele Miele, Claudio Conversano
2008 Advances in Evolutionary Algorithms  
The chapter also includes an appendix that presents J-Fast, a Java-based software for Decision Tree that currently implements Genetic Algorithms and Ant Colony Optimization.  ...  Because of their top-down binary splitting approach, decision trees can easily be converted into IF-THEN rules and used for decision making purposes.  ...  for the knowledge extraction and the decision making support.  ... 
doi:10.5772/6117 fatcat:4k577srk3vfv5ncl7xisv7ptm4

An Evolutionary Approach to Provide Flexible Decision Dialogues in Intelligent Decision Support Systems

Flávio R.S. Oliveira, Fernando B. Lima Neto
2008 2008 Eighth International Conference on Hybrid Intelligent Systems  
Comprising an important class of such tools, Intelligent Decision Support Systems (iDSS) are able to not only help on the decision making process, but also improve their performance through time.  ...  This work puts it out an interaction model based on evolutionary computation that is able to provide semi-automatic parameterization of decision trees of iDSS.  ...  Evolutionary Strategy in Decision Tree Classification Aitkenhead proposes an evolutionary algorithm to create DT for classification purposes [9] .  ... 
doi:10.1109/his.2008.166 dblp:conf/his/OliveiraN08 fatcat:5libbzlbxramjgwaadi2lo3m5m

A Review on Evolutionary Feature Selection

Nadia Abd-Alsabour
2014 2014 European Modelling Symposium  
This paper covers the first part only of the evolutionary algorithms for the feature selection problem due to the limitation of the number of pages.  ...  This paper presents a review of some of the most recent evolutionary algorithms used for solving feature selection based upon previously published research on feature selection.  ...  Bagging is an ensemble technique where many classifiers are built and the final classification decision is made based on some forms of voting of the committee of classifiers.  ... 
doi:10.1109/ems.2014.28 dblp:conf/ems/Abd-Alsabour14 fatcat:bzl4mgnbifakdgpy5ezwlg5gvi

FEATURES OF METAHEURISTIC METHODS

Євгеній Селютін, Ігор Козін
2021 Young Scientist  
A modification of the method of mixed jumping frogs for finding suboptimal solutions of the classification problem is proposed.  ...  On the basis of fragmentary structure it is proposed to use evolutionary algorithm. The prospect of using a genetic algorithm to find the best classifications was evaluated.  ...  One of the most common mathematical problems of today is classification. It arises in the analysis of research results, in the design and forecasting, in the evaluation and decision-making.  ... 
doi:10.32839/2304-5809/2021-2-90-21 fatcat:pvwsugmbjbcnfo6cubafrlyd7m

Using bayesian networks for selecting classifiers in GP ensembles

Claudio De Stefano, Gianluigi Folino, Francesco Fontanella, Alessandra Scotto di Freca
2011 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11  
The proposed system is able to learn and combine decision tree ensembles effectively by using two different strategies: in the first, decision tree ensembles are learned by means of a boosted GP algorithm  ...  This paper presents a novel approach for combining GP-based ensembles by means of a Bayesian Network.  ...  In [11] the learning of the BN is performed by means of an evolutionary algorithm using a direct encoding scheme of the BN structure (DAG).  ... 
doi:10.1145/2001858.2001955 dblp:conf/gecco/StefanoFFF11 fatcat:wrbguafllbd2bfftlwfzn6u52y

Evolutionary approach to optimisation of the operation of electric power distribution networks

Jan Stepien, Sylwester Filipiak
2013 International Journal of Advanced Computer Science and Applications  
An idea of using a classifying system and coevolutionary algorithm for operation support of electric power distribution systems operators has been presented in the paper.  ...  It is the use by the classifying system working with the co-evolution algorithm that enables the effective creation of substitute scenarios for the Medium Voltage electric power distribution network.  ...  In order to improve the performance obtained by evolutionary algorithms in distribution system reconfiguration problems, a tree encoding based on graph chains, called graph chains representation, and its  ... 
doi:10.14569/ijacsa.2013.040602 fatcat:yjzrfxd3f5c5lcnyu3xaza6roy

A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems

Veera Babu Ramakurthi, V. K. Manupati, José Machado, Leonilde Varela
2021 Applied Sciences  
An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability  ...  To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification  ...  Manufacturing decision making (MADEMA) approach was proposed for the assignment of work center resources with multiple decision-making criteria in order to have an effective utilization for IPPS problem  ... 
doi:10.3390/app11146314 fatcat:pqb3i4nj4jhoblrauhhq2jtgma

Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

A. Khan, A.R. Baig
2015 Journal of Applied Research and Technology  
This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem.  ...  In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier.  ...  using genetic algorithm with particle swarm optimization [24] , hybrid evolutionary algorithm [26] , and using multi-objective approaches for heuristic optimization [27] for optimization and classification  ... 
doi:10.1016/s1665-6423(15)30013-4 fatcat:xi5wk3ke6jhyrmljabpadg66fu
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