Algorithms for clustering data
Algorithms for clustering data
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Modern Information Retrieval
Information Visualization in Data Mining and Knowledge Discovery
Information Visualization in Data Mining and Knowledge Discovery
Introduction to Algorithms
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Pre-pruning Classification Trees to Reduce Overfitting in Noisy Domains
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Simplifying decision trees: A survey
The Knowledge Engineering Review
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis
Neural Processing Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Introduction to Information Retrieval
Introduction to Information Retrieval
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
Self-Organizing trees and forests: a powerful tool in pattern clustering and recognition
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Editorial: data mining in electronic commerce - support vs. confidence
Journal of Theoretical and Applied Electronic Commerce Research
Hi-index | 0.01 |
Years of research in the field of Pattern Recognition (PR) has led to scores of algorithms which can achieve supervised pattern classification. Such algorithms assume the knowledge of well-defined training sets with a clear specification of the identity of all the training samples. However, more recently, a new stream has emerged, namely, the so-called ''semi-supervised'' paradigm, i.e., one that uses a combination of labeled and unlabeled samples to perform classification [41]. Classifiers based on the latter, do not demand the specification of the class labels of every sample. Rather, a clustering-like mechanism processes the manifold, and attempts to distinguish the training samples into the separate classes, subsequent to which a supervised classifier is derived using a small subset of the training samples whose class identities are known. In this paper we will venture to utilize the Tree-based Topology Oriented SOM (TTOSOM) [3] for semi-supervised pattern classification. We first train a TTOSOM in which the neurons collectively obey the stochastic, topological and structural distribution of all the classes. Subsequently, we make use of the information provided in the labeled dataset. By using this information, we assign a class label to every single node in the Neural Network (NN), which, in turn, partitions the space into its Voronoi regions. On receiving the testing data, the task at hand is rather straightforward. One nearly determines the closest neuron to the testing sample and assigns the sample to the corresponding class. The complexity of the testing is linear, not in cardinality of the training set, but rather in the size of the TTOSOM tree! Our experimental results show that on average, the classification capabilities of our proposed strategy, even with a small number of neurons, are reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes.