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 …
order to improve the classification performance. Feature construction is particularly important …
Reusing genetic programming for ensemble selection in classification of unbalanced data
Classification algorithms can suffer from performance degradation when the class
distribution is unbalanced. This paper develops a two-step approach to evolving ensembles …
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
Dynamic flexible job shop scheduling is an important but difficult combinatorial optimisation
problem that has numerous real-world applications. Genetic programming has been widely …
problem that has numerous real-world applications. Genetic programming has been widely …
Two-tier genetic programming: Towards raw pixel-based image classification
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 …
such as object recognition and face detection. Conventional methods build classifiers based …
A genetically optimized neural network model for multi-class classification
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 …
Classification algorithms are generally binary in nature and must be extended for multi-class …
Tracking bad updates in mobile apps: A search-based approach
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 …
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
Modern genetic programming (GP) operates within the statistical machine learning (SML)
framework. In this framework, evolution needs to balance between approximation of an …
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 …
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 …
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
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 …
traditional data mining methods rely on the model assumption, they generally can suffer from …