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An Evolutionary Algorithm for Making Decision Graphs for Classification Problems
2016
Journal of Robotics, Networking and Artificial Life (JRNAL)
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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]
2012
Real-World Applications of Genetic Algorithms
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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
2018
Zenodo
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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]
2018
Computing in science & engineering (Print)
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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
2015
International Journal of Computer Applications
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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
2008
Decision Support Systems
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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
2008
Zenodo
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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]
2008
Advances in Evolutionary Algorithms
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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
2008
2008 Eighth International Conference on Hybrid Intelligent Systems
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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
2014
2014 European Modelling Symposium
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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
2021
Young Scientist
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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
2011
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11
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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
2013
International Journal of Advanced Computer Science and Applications
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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
2021
Applied Sciences
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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
2015
Journal of Applied Research and Technology
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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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