Single Classifier-based Multiple Classification Scheme for weak classifiers: An experimental comparison

Authors:
Albert Hung-Ren Ko;Robert Sabourin
Affiliations:
Laboratoire de communication et d'intégration de la microectronique, ícole de Technologie Supérieure, University of Quebec, 1100 Notre-Dame West Street, Montreal, Quebec, Canada H3C ...;Laboratoire d'imagerie, de vision et d'intelligence artificielle, ícole de Technologie Supérieure, University of Quebec, 1100 Notre-Dame West Street, Montreal, Quebec, Canada H3C 1K3
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 19
Cited 0

Local learning algorithms

Neural Computation
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting in the limit: maximizing the margin of learned ensembles

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Theoretical Study on Six Classifier Fusion Strategies

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Analysis: A Computer Oriented Approach

Statistical Analysis: A Computer Oriented Approach
Training Invariant Support Vector Machines

Machine Learning
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine Learning
From dynamic classifier selection to dynamic ensemble selection

Pattern Recognition
A Theoretical Analysis of Bagging as a Linear Combination of Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
A dynamic overproduce-and-choose strategy for the selection of classifier ensembles

Pattern Recognition
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental construction of classifier and discriminant ensembles

Information Sciences: an International Journal
Leave-One-Out-Training and Leave-One-Out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: A study on the performances of dynamic classifier selection based on local accuracy estimation

Pattern Recognition
Learn++.MF: A random subspace approach for the missing feature problem

Pattern Recognition
A Measure of Competence Based on Randomized Reference Classifier for Dynamic Ensemble Selection

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this paper, we propose a Single Classifier-based Multiple Classification Scheme (SMCS) that uses only a single classifier to generate multiple classifications for a given test data point. The SMCS does not require the presence of multiple classifiers, and generates diversity through the creation of pseudo test samples. The pseudo test sample generation mechanism allows the SMCS to adapt to dynamic environments without multiple classifier training. Moreover, because of the presence of multiple classifications, classification combination schemes, such as majority voting, can be applied, and so the mechanism may improve the recognition rate in a manner similar to that of Multiple Classifier Systems (MCS). The experimental results confirm the validity of the proposed SMCS as applicable to many classification systems. Even without parameter selection, the average performance of the SMCS is still comparable to that of Bagging or Boosting. Moreover, the SMCS and the traditional MCS scheme are not mutually exclusive, and the SMCS can be applied along with traditional MCS, such as Bagging and Boosting.