A survey on the application of genetic programming to classification (original) (raw)
Related papers
A Novel Genetic Programming Based Approach for Classification Problems
Lecture Notes in Computer Science, 2005
A new genetic programming based approach to classification problems is proposed. Differently from other approaches, the number of prototypes in the classifier is not a priori fixed, but automatically found by the system. In fact, in many problems a single class may contain a variable number of subclasses. Hence, a single prototype, may be inadequate to represent all the members of the class. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature.
Review of Classification Using Genetic Programming
International Journal of Engineering Science and …, 2010
Genetic programming (GP) is a powerful evolutionary algorithm introduced to evolve computer programs automatically. It is a domain independent, stochastic method with an important ability to represent programs of arbitrary size and shape. Its flexible nature has attracted numerous researchers in data mining community to use GP for classification. In this paper we have reviewed and analyzed tree based GP classification methods and propose taxonomy of these methods. We have also discussed various strengths and weaknesses of the technique and provide a framework to optimize the task of GP based classification.
Genetic Algorithm and Programming Based Classification: A Survey
Classification is the process of finding a model or a function that describes and distinguishes data classes and concepts, for the purpose of being able to use the model to predict the classes of objects whose class label is not known. The process of data analysis becomes time consuming and tedious as volume of data increases. So to make the process of data classification faster, soft computing techniques have been applied. Great deal of work has been done in the area of classification using evolutionary techniques. This survey gives an insight into the work done on classification using genetic algorithms and genetic programming and their applications in different problems and areas.
A Genetic Programming Approach to Binary Classification Problem
EAI Endorsed Transactions on Energy Web, 2018
The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solve this problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solve problems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data. Feature engineering was considered to improve the accuracy. In this research, feature transformation and feature creation were implemented. Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.
Generating Effective Classifiers with Supervised Learning of Genetic Programming
Lecture Notes in Computer Science, 2003
A new approach of learning classifiers using genetic programming has been developed recently. Most of the previous researches generate classification rules to classify data. However, the generation of rules is time consuming and the recognition accuracy is limited. In this paper, an approach of learning classification functions by genetic programming is proposed for classification. Since a classification function deals with numerical attributes only, the proposed scheme first transforms the nominal data into numerical values by rough membership functions. Then, the learning technique of genetic programming is used to generate classification functions. For the purpose of improving the accuracy of classification, we proposed an adaptive interval fitness function. Combining the learned classification functions with training samples, an effective classification method is presented. Numbers of data sets selected from UCI Machine Learning repository are used to show the effectiveness of the proposed method and compare with other classifiers.
A Novel Approach to Design Classifiers Using Genetic Programming
IEEE Transactions on Evolutionary Computation, 2004
We propose a new approach for designing classifiers for a -class ( 2) problem using genetic programming (GP). The proposed approach takes an integrated view of all classes when the GP evolves. A multitree representation of chromosomes is used. In this context, we propose a modified crossover operation and a new mutation operation that reduces the destructive nature of conventional genetic operations. We use a new concept of unfitness of a tree to select trees for genetic operations. This gives more opportunity to unfit trees to become fit. A new concept of OR-ing chromosomes in the terminal population is introduced, which enables us to get a classifier with better performance. Finally, a weight-based scheme and some heuristic rules characterizing typical ambiguous situations are used for conflict resolution. The classifier is capable of saying "don't know" when faced with unfamiliar examples. The effectiveness of our scheme is demonstrated on several real data sets.
Adaptive genetic programming applied to classification in data mining
2012
Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems, however, it suffers from a long run time. In addition, there are many parameters that need to be set before the GP is run. In this paper, we propose an adaptive GP that automatically determines the best parameters of a run, and executes the classification faster than standard GP. This adaptive GP has three variations. The first variant consists of an adaptive selection process ensuring that the produced solutions in the next generation are better than the solutions in the previous generation. The second variant adapts the crossover and mutation rates by modifying the probabilities ensuring that a solution with a high fitness is protected. And the third variant is an adaptive function list that automatically changes the functions used by deleting the functions that do not favorably contribute to the classification. These proposed variations were implemented and compared to the standard GP. The results show that a significant speedup can be achieved by obtaining similar classification accuracies.
Expert Systems with Applications, 2018
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • Automated design of Genetic Programming classification algorithms is presented. • Automated design uses a genetic algorithm and grammatical evolution. • The approach is trained and tested using real-world binary and multi-class data. • Grammatical evolution designed classifiers perform better for binary classification. • Genetic algorithm designed classifiers perform better for multi-classification.
Improving genetic programming classification for binary and multiclass datasets
2013
Genetic Programming (GP) is one of the evolutionary computation techniques that is used for the classification process. GP has shown that good accuracy values especially for binary classifications can be achieved, however, for multiclass classification unfortunately GP does not obtain high accuracy results. In this paper, we propose two approaches in order to improve the GP classification task. One approach (GP-K) uses the K-means clustering technique in order to transform the produced value of GP into class labels. The second approach (GP-D) uses a discretization technique to perform the transformation. A comparison of the original GP, GP-K and GP-D was conducted using binary and multiclass datasets. In addition, a comparison with other state-of-the-art classifiers was performed. The results reveal that GP-K shows good improvement in terms of accuracy compared to the original GP, however, it has a slightly longer execution time. GP-D also achieves higher accuracy values than the original GP as well as GP-K, and the comparison with the state-of-the-art classifiers reveal competitive accuracy values.