Multi Objective Genetic Programming for Feature Construction in Classification Problems

2011 ◽  
pp. 503-506
Author(s):  
Mauro Castelli ◽  
Luca Manzoni ◽  
Leonardo Vanneschi
Keyword(s):  
Genetic Programming ◽  
Feature Construction ◽  
Classification Problems ◽  
Multi Objective

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2020 ◽  
Author(s):  
João Batista ◽  
Ana Cabral ◽  
Maria Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic Programming (GP) is a powerful Machine Learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyper-feature from satellite bands to improve the classification of land cover types. We add the evolved hyper-features to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (Decision Trees, Random Forests and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyper-features to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI and NBR. We also compare the performance of the M3GP hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

scholarly journals Improving Land Cover Classification Using Genetic Programming

2020 ◽  
Author(s):  
João Batista ◽  
Ana Cabral ◽  
Maria Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Binary Classification ◽  
Multiclass Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models ◽  
Burnt Areas

Genetic Programming (GP) is a powerful Machine Learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several satellite images over different countries to perform binary classification of burnt areas and multiclass classification of land cover types. We add the evolved hyper-features to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (Decision Trees, Random Forests and XGBoost) on the multiclass classification datasets, with no significant effect on the binary classification ones. We show that adding the M3GP hyper-features to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI and NBR. We also compare the performance of the M3GP hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

scholarly journals Genetic programming for feature construction and selection in classification on high-dimensional data

2021 ◽  
Author(s):  
Binh Tran ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
High Dimensional Data ◽  
Classification Performance ◽  
High Dimensional ◽  
Feature Construction ◽  
Classification Problems ◽  
Memetic Computing ◽  
Dimensional Classification ◽  
High Level

Classification on high-dimensional data with thousands to tens of thousands of dimensions is a challenging task due to the high dimensionality and the quality of the feature set. The problem can be addressed by using feature selection to choose only informative features or feature construction to create new high-level features. Genetic programming (GP) using a tree-based representation can be used for both feature construction and implicit feature selection. This work presents a comprehensive study to investigate the use of GP for feature construction and selection on high-dimensional classification problems. Different combinations of the constructed and/or selected features are tested and compared on seven high-dimensional gene expression problems, and different classification algorithms are used to evaluate their performance. The results show that the constructed and/or selected feature sets can significantly reduce the dimensionality and maintain or even increase the classification accuracy in most cases. The cases with overfitting occurred are analysed via the distribution of features. Further analysis is also performed to show why the constructed feature can achieve promising classification performance. This is a post-peer-review, pre-copyedit version of an article published in 'Memetic Computing'. The final authenticated version is available online at: https://doi.org/10.1007/s12293-015-0173-y. The following terms of use apply: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use.

scholarly journals Genetic programming for feature construction and selection in classification on high-dimensional data

2021 ◽  
Author(s):  
Binh Tran ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
High Dimensional Data ◽  
Classification Performance ◽  
High Dimensional ◽  
Feature Construction ◽  
Classification Problems ◽  
Memetic Computing ◽  
Dimensional Classification ◽  
High Level

Classification on high-dimensional data with thousands to tens of thousands of dimensions is a challenging task due to the high dimensionality and the quality of the feature set. The problem can be addressed by using feature selection to choose only informative features or feature construction to create new high-level features. Genetic programming (GP) using a tree-based representation can be used for both feature construction and implicit feature selection. This work presents a comprehensive study to investigate the use of GP for feature construction and selection on high-dimensional classification problems. Different combinations of the constructed and/or selected features are tested and compared on seven high-dimensional gene expression problems, and different classification algorithms are used to evaluate their performance. The results show that the constructed and/or selected feature sets can significantly reduce the dimensionality and maintain or even increase the classification accuracy in most cases. The cases with overfitting occurred are analysed via the distribution of features. Further analysis is also performed to show why the constructed feature can achieve promising classification performance. This is a post-peer-review, pre-copyedit version of an article published in 'Memetic Computing'. The final authenticated version is available online at: https://doi.org/10.1007/s12293-015-0173-y. The following terms of use apply: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2020 ◽  
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic Programming (GP) is a powerful Machine Learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in Remote Sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs Feature Construction by evolving hyper-features from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyper-feature from satellite bands to improve the classification of land cover types. We add the evolved hyper-features to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (Decision Trees, Random Forests and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyper-features to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI and NBR. We also compare the performance of the M3GP hyper-features in the binary classification problems with those created by other Feature Construction methods like FFX and EFS.

scholarly journals Genetic Feature Construction Genetic Feature Construction: a parallel implementation of a genetic programming tool for feature construction

2019 ◽  
Vol 4 (5) ◽  
pp. 5-11
Author(s):  
Ioannis G Tsoulos ◽  
Alexandros Tzallas ◽  
Dimitrios Tsalikakis
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Genetic Programming ◽  
Parallel Implementation ◽  
Feature Construction ◽  
Rbf Neural Networks ◽  
Classification Problems ◽  
Parallel Genetic Algorithms ◽  
Programming Tool ◽  
Genetic Feature

In this paper a parallel implementation of a recently introduced method for feature construction is described. This technique utilizes parallel genetic algorithms along with RBF neural networks to create new features from the original ones by discovering the hidden relations between patterns. The method is tested on series of classification problems from a variety of areas and the results are reported. The accompanied software is written entirely in ANSI C++ using the well established MPI library for parallelization.

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