Genetic algorithm based feature selection for target detection in SAR images

Bhanu, Bir; Lin, Yingqiang

doi:10.1016/s0262-8856(03)00057-x

Cited by 100 publications

(43 citation statements)

References 15 publications

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“…Feature contrast brightness of blob (9) is the best one with recognition rate 0.98. To show the efficacy of CGP in synthesizing effective composite features, we consider three cases: only the worst two primitive features (blob inertia (6) and mean values of pixels within blob (8)) are used by CGP; five bad primitive features (blob inertia (6), mean values of pixels within blob Table 2 shows the maximum and minimum recognition rates in these experiments, where tr and te mean training and testing and max and min stand for the maximum and minimum recognition rates. , it is obvious that composite feature vectors synthesized by CGP are very effective in distinguishing object from clutter.…”

Section: Methodsmentioning

confidence: 99%

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Learning Features for Object Recognition

Lin

Bhanu

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Features represent the characteristics of objects and selecting or synthesizing effective composite features are the key factors to the performance of object recognition. In this paper, we propose a co-evolutionary genetic programming (CGP) approach to learn composite features for object recognition. The motivation for using CGP is to overcome the limitations of human experts who consider only a small number of conventional combinations of primitive features during synthesis. On the other hand, CGP can try a very large number of unconventional combinations and these unconventional combinations may yield exceptionally good results in some cases. Our experimental results with real synthetic aperture radar (SAR) images show that CGP can learn good composite features. We show results to distinguish objects from clutter and to distinguish objects that belong to several classes.

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Section: Methodsmentioning

confidence: 99%

“…• The Set of Terminals: The set of terminals used in this paper are 20 primitive features used in [6]. The first 10 of them are designed by MIT Lincoln lab to capture the particular characteristics of synthetic aperture radar (SAR) imagery and are found useful for object detection.…”

Section: Design Considerationsmentioning

confidence: 99%

Learning Features for Object Recognition

Lin

Bhanu

2003

Genetic and Evolutionary Computation — GECCO 2003

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“…A wide range of wrapper-based methods have been used including forward selection [9]; backward elimination [10]; hill-climbing [11]; branch and bound algorithms [12]; simulated annealing and genetic algorithms (GAs) [13,14,15,16]. Kudo and Sklansky [17] made a comparison among many of the feature selection algorithms and explicitly recommended that GAs should be used for large-scale problems with more than 50 candidate variables.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Feature Selection Method Based on Fisher Score and Genetic Algorithm

2016

Jour. Math. Sci. Adv. Appl.

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Fisher score and genetic algorithm are widely used for feature selection.However, some redundant features will be selected by Fisher score, and the convergence properties may be worse if the initial population of genetic algorithm is generated by a random manner. To improve the performance of feature selection by Fisher score and genetic algorithm, we propose a hybrid feature selection method, which merging the advantages of Fisher score and genetic algorithm together. It aims at utilizing the features' Fisher score to generate the initial population of genetic algorithm. To begin with, the Fisher scores of all the features will be mapped into a specific interval by a linear function, and then the rescaled Fisher scores will be utilized to generate the initial population of genetic algorithm. Finally, the initial population will be used in the subsequent procedure of genetic algorithm to perform feature selection with elitist strategy for reference. In this paper, we choose four data sets of Sonar, WDBC, Arrhythmia, and Hepatitis to test the performance of our algorithm. Feature subsets of the four data sets will be selected by our algorithm, and then the dimensionality of data sets will be reduced according to the selected feature subsets, respectively. 1-NN classifier is used to classify the dimensionality reduced data sets, and respectively, achieving the classification MI ZHOU 52 accuracy of 72.36%, 95.64%, 72.04%, and 87.83% with ten-fold cross validation method. The experiment results show that, compared to the performance of Fisher score, genetic algorithm and Fisher score genetic algorithm, our algorithm is fit for eliminate redundant features, and can select discriminative features. Above all, our method is effective in feature selection.

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“…PSO is similar to the genetic algorithms (GAs) in the sense that these two heuristics are population-based search techniques, namely, PSO and GA operate on a population (swarm) and transform it to another set of population in a single iteration with likely improvement using a combination or deterministic and probabilistic rules. PSO is quite often compared with GA [13][14][15][16][17]. Interestingly, most of the literature is concerned with simple comparative scenarios involving experiments exploiting numeric data.…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, most of the literature is concerned with simple comparative scenarios involving experiments exploiting numeric data. An interesting comparison of PSO and GA with a focus on dimensionality aspects of the problems has been offered in [13] and [15], respectively. Statistical comparison using t-test is presented in [16] for several benchmarks.…”

Section: Introductionmentioning

confidence: 99%

Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

Park¹,

Oh²

2012

Journal of Electrical Engineering and Technology

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-In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

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Genetic algorithm based feature selection for target detection in SAR images

Cited by 100 publications

References 15 publications

Learning Features for Object Recognition

Learning Features for Object Recognition

A Hybrid Feature Selection Method Based on Fisher Score and Genetic Algorithm

Prototype-based Classifier with Feature Selection and Its Design with Particle Swarm Optimization: Analysis and Comparative Studies

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