A GA-Based Feature Selection Algorithm for Remote Sensing Images

Stefano, Claudio De; Fontanella, Francesco; Marrocco, Claudio

doi:10.1007/978-3-540-78761-7_29

Cited by 20 publications

(7 citation statements)

References 11 publications

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“…The goals are often to learn different bio-metrics: facial features[Fun+97; Har+05; Vig+12; Liu+08; Nes+09], gesture/activity recognition [Cha+13] and iris recognition [Roy+08]. There are also studies where the goal is generic object recognition or detection of some sort [Che+12;DS+08;Tre+04;Dat+11].…”

Section: Visual Featuresmentioning

confidence: 99%

Evolutionary Feature Selection

Dreyer¹

2017

Encyclopedia of Machine Learning and Data Mining

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This thesis contains research on feature selection, in particular feature selection using evolutionary algorithms. Feature selection is motivated by increasing data-dimensionality and the need to construct simple induction models. A literature review of evolutionary feature selection is conducted. After that a abstract feature selection algorithm, capable of using many different wrappers, is constructed. The algorithm is configured using a low-dimensional dataset. Finally it is tested on a wide range of datasets, revealing both it's abilities and problems. The main contribution is the revelation that classifier accuracy is not a sufficient metric for feature selection on high-dimensional data. Preface This thesis is the intellectual product of Sigve Dreyer, at the artificial intelligence group at the department of computer and information science at NTNU. I would like to thank Anders Kofod-Petersen for his supervision and guidance. I also give credit to Bjoern Magnus Mathisen for providing a high performance testing environment.

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Section: Visual Featuresmentioning

confidence: 99%

Evolutionary Feature Selection

Dreyer¹

2017

Encyclopedia of Machine Learning and Data Mining

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“…Step 4: do feature selection using the first evaluation criterion Seq (1), if the loose stop condition is met, then go to Step 5…”

Section: Study Of Search Algorithmmentioning

confidence: 99%

“…Seq(i + 1)), then turn to Steps 2-5, then turn to Step 10 selection method is O(npg), and the computational complexity of SMCFS is O(kpg), where p is size of population, g is number of search generations and n is number of evaluation criteria. The k is one coefficient and meets the formula: 1 < k < n. Through lots of experiments, usually the domain of k is [1,2].…”

Section: Computational Complexitymentioning

confidence: 99%

“…In many pattern classification applications, selection of the most characterizing features (or attributes) of the observed data (such as feature selection or variable selection, among many other names) is important to maximize the classification accuracy [1][2][3][4][5][6][7][8][9]. This is especially important when one Y. Li ( ) · X. Zeng College of Communication Engineering, Chongqing University, Chongqing 400030, China e-mail: lymcentor924924@gmail.com is required to deal with a large or even overwhelming feature set.…”

Section: Introductionmentioning

confidence: 99%

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Sequential multi-criteria feature selection algorithm based on agent genetic algorithm

Zeng

2008

Appl Intell

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A multi-criteria feature selection method-sequential multi-criteria feature selection algorithm (SMCFS) has been proposed for the applications with high precision and low time cost. By combining the consistency and otherness of different evaluation criteria, the SMCFS adopts more than one evaluation criteria sequentially to improve the efficiency of feature selection. With one novel agent genetic algorithm (chain-like agent GA), the SMCFS can obtain high precision of feature selection and low time cost that is similar as filter method with single evaluation criterion. Several groups of experiments are carried out for comparison to demonstrate the performance of SMCFS. SMCFS is compared with different feature selection methods using three datasets from UCI database. The experimental results show that the SM-CFS can get low time cost and high precision of feature selection, and is very suitable for this kind of applications of feature selection.

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“…In [38], GA-based feature selection algorithm is used to select a set of discriminative feature set for satellite images. Separability index is used as the fitness function to evaluate feature subsets and the effectiveness of the algorithm is tested on a neural network classifier.…”

Section: Introductionmentioning

confidence: 99%

Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover

Barb

Kilicay‐Ergin

2013

IJGI

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Abstract:Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult to build due to factors that affect training complexity, such as coexistence of visual patterns in same images, over-fitting or under-fitting and semantic representation differences among image analysts. This article proposes a methodology to reduce the complexity of ranking satellite images for associative methods. Our approach employs genetic operations to provide faster and more accurate models for ranking by semantic using low level features. The added accuracy is provided by a reduction in the likelihood to reach local minima or to overfit. The experiments show that, using genetic optimization, associative methods perform better or at similar levels as state-of-the-art ensemble methods for ranking. The mean average precision (MAP) of ranking by semantic was improved by 14% over similar associative methods that use other optimization techniques while maintaining smaller size for each semantic model.

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A GA-Based Feature Selection Algorithm for Remote Sensing Images

Cited by 20 publications

References 11 publications

Evolutionary Feature Selection

Evolutionary Feature Selection

Sequential multi-criteria feature selection algorithm based on agent genetic algorithm

Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover

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