Classifier design with feature selection and feature extraction using layered genetic programming
Section snippets
Feature selection
This research concentrate on three research topics: feature selection, feature generation, and classifier design. Feature selection is an important technique of pattern recognition dealing with raw features. It focuses on removing useless, irrelevant, and redundant features. The classification accuracy of data derived by selected features is better than that by no selection. Many research working on feature selection have been proposed (Ahmad and Dey, 2005, Dash and Liu, 1997, Jain and Zongker,
FLGP
This section aims to itemize FLGP. At first, basic GP terms, including terminal, operation, individual, population, and genetic operators are going to be introduced. Secondly, layers and the relations between layers are described.
We define the classification problem as follows.
Let T be the training set for a K-class classification problem including n training samples and TS be the test set. A training sample of T is a pair of class label and m significant real-valued elements
Experiments
This section will discuss the experiments and analyzes classification results. We select three diagnostic problems, cancer, diabetes, and heart, from the PROBEN1 benchmark set (Prechelt, 1994). These problems are originally from the UCI repository (Blake, Keogh, & Merz, 1998) and have been preprocessed by Prechelt (1994). Values of all sets are normalized to the continuous range [0, 1]. Missing attributes are completed. Every attribute of m possible values is encoded by the 1-of-m method.
Conclusions
This paper proposes a novel method called FLGP to construct classifier with capabilities of feature selection and feature extraction. FLGP employs multi-population genetic programming technique in a proper multi-layer architecture. By means of a number of experiments, we show that FLGP not only achieves high classification accuracy but also completes feature selection and feature extraction simultaneously. The classification accuracy of FLGP is comparable to traditional single population
References (41)
- et al.
A feature selection technique for classificatory analysis
Pattern Recognition Letters
(2005) - et al.
Feature selection for classification
Intelligent Data Analysis
(1997) - et al.
Wrappers for feature subset selection
Artificial Intelligence
(1997) - et al.
Comparison of algorithms that select features for pattern classifiers
Pattern Recognition
(2000) - et al.
Two realizations of a general feature extraction framework
Pattern Recognition
(2004) - et al.
Nonlinear feature extraction based on centroids and kernel functions
Pattern Recognition
(2004) Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
(2005)- et al.
Floating search methods in feature selection
Pattern Recognition Letters
(1994) - et al.
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
(1989) - et al.
Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition
Pattern Recognition
(2003)
Genetic programming: an introduction on the automatic evolution of computer programs and its application
A comparison of linear genetic programming and neural networks in medical data mining
IEEE Transactions on Evolutionary Computation
Learning effective classifiers with Z-value measure based on genetic programming
Pattern Recognition
Discovering interesting classification rules with genetic programming
Applied Soft Computing
An empirical study of multipopulation genetic programming
Genetic Programming and Evolvable Machines
Data mining: concepts and techniques
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics
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2020, Applied Soft Computing JournalCitation Excerpt :The previous GP classifier construction methods focused on how to evaluate a GP classification rule, how to constraint the function complexity of a GP classification rule to obtain better interpretability, and how to design a GP classifier to perform multiclass classification problems. There are some researches on designing GP classifiers that simultaneously perform feature selection and classification [11,35,64]. However, the impact of irrelevant and redundant features on GP classifier is not verified in details, and whether constraining irrelevant and redundant features can improve the performance of GP classifier should be verified.
Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection
2019, Applied Soft Computing JournalCitation Excerpt :Much work was investigated the generalization ability of GP and ABC in classification problems [38–42]. Recently, works using automatic programming methods for high-dimensional symbolic regression problems have increased [36,38,43–46]. Lin et al. proposed a multi-population genetic programming-based Feature Layered Genetic Programming (FLGP) classifier using multi-layered architecture [43].