scholarly journals A CART-based Genetic Algorithm for Constructing Higher Accuracy Decision Trees

2020 ◽  
Author(s):  
Elif Ersoy ◽  
Erinç Albey ◽  
Enis Kayış
Keyword(s):  
Genetic Algorithm ◽  
Decision Trees

THE USE OF VERSION SPACE CONTROLLED GENETIC ALGORITHMS TO SOLVE THE BOOLE PROBLEM

1993 ◽  
Vol 02 (02) ◽  
pp. 219-234 ◽  
Author(s):  
ROBERT G. REYNOLDS ◽  
JONATHAN I. MALETIC
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Decision Trees ◽  
General Framework ◽  
Performance History ◽  
New Members ◽  
Symbiotic Relationships ◽  
Version Space ◽  
History Of ◽  
Better Than

The Version Space Controlled Genetic Algorithms (VGA) uses the structure of the version space to cache generalizations about the performance history of chromosomes in the genetic algorithm. This cached experience is used to constrain the generation of new members of the genetic algorithms population. The VGA is shown to be a specific instantiation of a more general framework, Autonomous Learning Elements (ALE). The capabilities of the VGA system are demonstrated using the Boole problem suggested by Wilson [Wilson 1987]. The performance of the VGA is compared to that of decision trees and genetic algorithms. The results suggest that the VGA is able to exploit a certain set of symbiotic relationships between its components, so that the resulting system performs better than either component individually.

Combination classification method for customer relationship management

2019 ◽  
Vol 32 (5) ◽  
pp. 1004-1022
Author(s):  
Zhe Zhang ◽  
Yue Dai
Keyword(s):  
Genetic Algorithm ◽  
Decision Trees ◽  
Customer Relationship Management ◽  
Predictive Accuracy ◽  
Behavior Pattern ◽  
Relationship Management ◽  
Customer Relationship ◽  
Combination Method ◽  
Classification Problems ◽  
Content Type

Purpose For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm. Design/methodology/approach In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm. Findings The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results. Originality/value The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.

DMiner-I: A software tool of data mining and its applications

Robotica ◽  
2002 ◽  
Vol 20 (5) ◽  
pp. 499-508
Author(s):  
Jie Yang ◽  
Chenzhou Ye ◽  
Nianyi Chen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Knowledge Representation ◽  
Decision Trees ◽  
Software Tool ◽  
Support Vector ◽  
Function Models

SummaryA software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized byVisual C++under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

scholarly journals Development of the classifier based on a multilayer perceptron using genetic algorithm and cart decision tree

2021 ◽  
Vol 5 (9 (113)) ◽  
pp. 82-90
Author(s):  
Lyudmila Dobrovska ◽  
Olena Nosovets
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Tree ◽  
Decision Trees ◽  
Multilayer Perceptron ◽  
Data Sets ◽  
Biomedical Data ◽  
Simulation Accuracy ◽  
The Neural Network

The problem of developing universal classifiers of biomedical data, in particular those that characterize the presence of a large number of parameters, inaccuracies and uncertainty, is urgent. Many studies are aimed at developing methods for analyzing these data, among them there are methods based on a neural network (NN) in the form of a multilayer perceptron (MP) using GA. The question of the application of evolutionary algorithms (EA) for setting up and learning the neural network is considered. Theories of neural networks, genetic algorithms (GA) and decision trees intersect and penetrate each other, new developed neural networks and their applications constantly appear. An example of a problem that is solved using EA algorithms is considered. Its goal is to develop and research a classifier for the diagnosis of breast cancer, obtained by combining the capabilities of the multilayer perceptron using the genetic algorithm (GA) and the CART decision tree. The possibility of improving the classifiers of biomedical data in the form of NN based on GA by applying the process of appropriate preparation of biomedical data using the CART decision tree has been established. The obtained results of the study indicate that these classifiers show the highest efficiency on the set of learning and with the minimum reduction of Decision Trees; increasing the number of contractions usually degrades the simulation result. On two datasets on the test set, the simulation accuracy was »83–87 %. The experiments carried out have confirmed the effectiveness of the proposed method for the synthesis of neural networks and make it possible to recommend it for practical use in processing data sets for further diagnostics, prediction, or pattern recognition

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