Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Global Optimization
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
A Combined Ant Colony and Differential Evolution Feature Selection Algorithm
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Differential evolution based band selection in hyperspectral data classification
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
IEEE Transactions on Evolutionary Computation
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Hyperspectral images are captured from hundreds of narrow and contiguous bands from the visible to infrared regions of electromagnetic spectrum. Each pixel of an image is represented by a vector where the components of the vector constitute the reflectance value of the surface for each of the bands. The length of the vector is equal to the number of bands. Due to the presence of large number of bands, classification of hyperspectral images becomes computation intensive. Moreover, higher correlation among neighboring bands increases the redundancy among them. As a result, feature selection becomes very essential for reducing the dimensionality. In the proposed work, an attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms. Self-adaptive differential evolution (SADE) is used for feature subset generation. Generated subsets are evaluated using a wrapper model where fuzzy k-nearest neighbor classifier is taken into consideration. Our proposed method also uses a feature ranking technique, ReliefF algorithm, for removing duplicate features. To demonstrate the effectiveness of the proposed method, investigation is carried out on three sets of data and the results are compared with four other evolutionary based state-of-the-art feature selection techniques. The proposed method shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.