PROJECT TITLE:

Ensemble of Adaptive Rule-Based Granular Neural Network Classifiers for Multispectral Remote Sensing Images - 2015

ABSTRACT:

Data granulation opens ample scope to design probably clear neural networks known as granular neural networks (GNNs). The paper proposes a classification model in the framework of ensemble of GNN-primarily based classifiers, and justifies its improved performance in classifying land use/cowl classes of multispectral remote sensing (RS) pictures. The model additionally provides an adaptive method for fuzzy rules extraction from the fuzzified input variables for GNN and therefore avoid the uncertainty in empirical search of rules for output category labels. The superiority of the proposed model to other similar methods is established both visually and quantitatively for land use/cowl classification of multispectral RS pictures. Comparative analysis revealed that GNN with multiple rules performed higher than GNN with single rule assigned for every of the categories, and ensemble of GNNs outperformed all other strategies. Varied performance measures, like overall accuracy, producer's accuracy, user's accuracy, kappa coefficient, and live of dispersion estimation, are used for comparative analysis.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Short-Term Traffic Flow Forecasting Using Ensemble Approach Based on Deep Belief Networks ABSTRACT: Transportation services are playing an increasingly important role in people's day-to-day lives, and they bring
PROJECT TITLE : Semisupervised Multiple Choice Learning for Ensemble Classification ABSTRACT: Due to the fact that it is so effective at enhancing the predictive performance of classification models, ensemble learning has a wide
PROJECT TITLE : Unsupervised Ensemble Classification with Sequential and Networked Data ABSTRACT: Ensemble learning, a paradigm of machine learning in which multiple models are combined, has shown promising performance in a variety
PROJECT TITLE : Global Negative Correlation Learning A Unified Framework for Global Optimization of Ensemble Models ABSTRACT: The field of machine learning makes extensive use of ensembles as an approach, and the diversity that
PROJECT TITLE : A heterogeneous ensemble learning method for neuroblastoma survival prediction ABSTRACT: Neuroblastoma is a form of childhood cancer that has a high fatality and incidence rate. An accurate prognosis of the

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry