Transductive Methods for the Distributed Ensemble Classification Problem

Authors:
David J. Miller;Siddharth Pal
Affiliations:
millerdj@ee.psu.edu;Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802-2701, sup111@psu.edu
Venue:
Neural Computation
Year:
2007

Citing 13
Cited 5

Elements of information theory

Elements of information theory
Bagging predictors

Machine Learning
Inducing Features of Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal approximation of discrete probability distribution with kth-order dependency and its application to combining multiple classifiers

Pattern Recognition Letters
Distributed Detection and Data Fusion

Distributed Detection and Data Fusion
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure

Neural Computation
Transductive Inference for Text Classification using Support Vector Machines

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A classification paradigm for distributed vertically partitioned data

Neural Computation
Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms

Neural Computation
Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification

Neural Computation
Critic-driven ensemble classification

IEEE Transactions on Signal Processing
Distributed classification of Gaussian space-time sources in wireless sensor networks

IEEE Journal on Selected Areas in Communications

An Extension of Iterative Scaling for Decision and Data Aggregation in Ensemble Classification

Journal of VLSI Signal Processing Systems
Non-stationary data sequence classification using online class priors estimation

Pattern Recognition
Extensions of transductive learning for distributed ensemble classification and application to biometric authentication

Neurocomputing
Protein Fold Prediction Problem Using Ensemble of Classifiers

ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Transfer estimation of evolving class priors in data stream classification

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider ensemble classification for the case where there is no common labeled training data for jointly designing the individual classifiers and the function that aggregates their decisions. This problem, which we call distributed ensemble classification, applies when individual classifiers operate (perhaps remotely) on different sensing modalities and when combining proprietary or legacy classifiers. The conventional wisdom in this case is to apply fixed rules of combination such as voting methods or rules for aggregating probabilities. Alternatively, we take a transductive approach, optimizing the combining rule for an objective function measured on the unlabeled batch of test data. We propose maximum likelihood (ML) objectives that are shown to yield well-known forms of probabilistic aggregation, albeit with iterative, expectation-maximization-based adjustment to account for mismatch between class priors used by individual classifiers and those reflected in the new data batch. These methods are extensions, for the ensemble case, of the work of Saerens, Latinne, and Decaestecker (2002). We also propose an information-theoretic method that generally outperforms the ML methods, better handles classifier redundancies, and addresses some scenarios where the ML methods are not applicable. This method also well handles the case of missing classes in the test batch. On UC Irvine benchmark data, all our methods give improvements in classification accuracy over the use of fixed rules when there is prior mismatch.