Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
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Many credit data classification problems require label predictions only for a given unlabeled test set. Since the number of an available unlabeled test data set is much larger than a labeled data set, it is desirable to build a predictive model in a transductive setting that takes advantage of the unlabeled data as well as labeled data. This paper proposes a localized transduction based multi-layer perceptron (MLP) methodology to build a better classifier. We provide a practical framework for our methodology. Simulations on real credit delinquents detection problems are conducted to test the proposed method with a promising result.