A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming

K Neshatian, M Zhang… - IEEE Transactions on …, 2012 - ieeexplore.ieee.org
Feature construction is an effort to transform the input space of classification problems in
order to improve the classification performance. Feature construction is particularly important …

Reusing genetic programming for ensemble selection in classification of unbalanced data

U Bhowan, M Johnston, M Zhang… - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
Classification algorithms can suffer from performance degradation when the class
distribution is unbalanced. This paper develops a two-step approach to evolving ensembles …

Genetic programming for dynamic flexible job shop scheduling: Evolution with single individuals and ensembles

M Xu, Y Mei, F Zhang, M Zhang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Dynamic flexible job shop scheduling is an important but difficult combinatorial optimisation
problem that has numerous real-world applications. Genetic programming has been widely …

Two-tier genetic programming: Towards raw pixel-based image classification

H Al-Sahaf, A Song, K Neshatian, M Zhang - Expert Systems with …, 2012 - Elsevier
Classifying images is of great importance in machine vision and image analysis applications
such as object recognition and face detection. Conventional methods build classifiers based …

A genetically optimized neural network model for multi-class classification

A Bhardwaj, A Tiwari, H Bhardwaj… - Expert Systems with …, 2016 - Elsevier
Multi-class classification is one of the major challenges in real world application.
Classification algorithms are generally binary in nature and must be extended for multi-class …

Tracking bad updates in mobile apps: A search-based approach

I Saidani, A Ouni, M Ahasanuzzaman, S Hassan… - Empirical Software …, 2022 - Springer
The rapid growth of the mobile applications development industry raises several new
challenges to developers as they need to respond quickly to the users' needs in a world of …

A survey of statistical machine learning elements in genetic programming

A Agapitos, R Loughran, M Nicolau… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Modern genetic programming (GP) operates within the statistical machine learning (SML)
framework. In this framework, evolution needs to balance between approximation of an …

Enhanced feature selection for biomarker discovery in LC-MS data using GP

S Ahmed, M Zhang, L Peng - 2013 IEEE congress on …, 2013 - ieeexplore.ieee.org
Biomarker detection in LC-MS data depends mainly on feature selection algorithms as the
number of features is extremely high while the number of samples is very small. This makes …

Extracting image features for classification by two-tier genetic programming

H Al-Sahaf, A Song, K Neshatian… - 2012 IEEE Congress on …, 2012 - ieeexplore.ieee.org
Image classification is a complex but important task especially in the areas of machine vision
and image analysis such as remote sensing and face recognition. One of the challenges in …

Multiclass classification on high dimension and low sample size data using genetic programming

T Wei, WL Liu, J Zhong, YJ Gong - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Multiclass classification is one of the most fundamental tasks in data mining. However,
traditional data mining methods rely on the model assumption, they generally can suffer from …