Instance-Based Learning Algorithms
Machine Learning
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Maximizing Text-Mining Performance
IEEE Intelligent Systems
Automatic Text Categorization: Case Study
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks
Proceedings of the 24th international conference on Machine learning
Scaling up text classification for large file systems
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification
IEEE Transactions on Knowledge and Data Engineering
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
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To make efficient decisions, knowledge in terms of experience is needed that can be obtained from the process of learning. The present paper's aim and objective are to explore the learning process in text classification using semi-supervised learning paradigm and compare the results obtained with the supervised learning classifier's accuracy. Semi-supervised learning can be applied when limited amount of training data is available. In traditional K-nearest neighbour algorithm all features are given similar weights in all classes which is not reasonable. Few features may play vital role in some classes and in others there presence has no impact. In the present paper, exploration of assigning different weights to the features in different classes based on the concept of variance is discussed. Finally to gain insight in semi-supervised learning paradigm, supervised and semi-supervised learning paradigm in text classification are compared. Results obtained show that the semi-supervised learning paradigm can be applied in cases where very limited training data is available, but still reasonable classifier accuracy can be obtained.