Swarm intelligence
Self-Organizing Maps
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Ant colony optimization for logistic regression and its application to wine quality assessment
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Revisiting the Foundations of Artificial Immune Systems for Data Mining
IEEE Transactions on Evolutionary Computation
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This paper presents an improved Artificial Immune System (AIS) approach for unsupervised classification in multispectral remote-sensing imagery. For benchmarking, one has considered several unsupervised nature-inspired intelligent classifiers (AIS, neural, fuzzy) versus statistical ones. We have comparatively evaluated the following pattern recognition techniques: the proposed AIS model; Self-Organizing Map (SOM); Vector Quantization SOM (VQSOM); Fuzzy C-means, and K-means. The considered techniques have been evaluated using both synthetic and real datasets. The real datasets correspond to the LANDSAT 7 ETM+ multispectral image (341 × 343 pixels) taken in June 2000, representing a region of Bucharest, Romania. There have been considered four pattern classes: artificial surfaces, agricultural area, forest, water. One has also evaluated the case of choosing a balanced dataset from the LANDSAT image, with equal number of 800 selected multispectral pixels per class. For the balanced LANDSAT dataset with 3 bands (1, 4, 5), the best experimental correct recognition score is of 93.78% for AIS model followed by the scores of 89.09% for the 5 × 5 neuron SOM model, 83.28% for VQSOM, 84.18% for Fuzzy C-means, and 83.15% for K-means.