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 variety of applications that have been met with great success. In this article, we propose an approach known as semisupervised multiple choice learning (SemiMCL) for jointly training a network ensemble using data that is only partially labeled. Our model places a primary emphasis on enhancing a labeled data assignment among the constituent networks and utilizing unlabeled data to better capture domain-specific information. This is done in order to effectively facilitate semisupervised classification, which is the model's ultimate goal. In contrast to more traditional learning models based on multiple choice questions, the constituent networks acquire knowledge of multiple tasks during the training process. In particular, a secondary reconstruction task is incorporated in order to acquire knowledge of domain-specific representation. When minimizing the conditional entropy with respect to the posterior probability distribution, we use a negative l1 -norm regularization in order to achieve our goal of performing implicit labeling on reliable unlabeled samples. This allows us to achieve the best possible results. In order to prove that the proposed SemiMCL model is both effective and superior to other models, a large number of experiments are run on a variety of datasets that come from the real world.


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 : 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
PROJECT TITLE : A Novel Method for Creating an Optimized Ensemble Classifier by Introducing Cluster Size Reduction and Diversity ABSTRACT: Within the scope of this research project, a novel approach to generating an improved

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

Project Enquiry