15 research outputs found
Learning from unequally reliable blind ensembles of classifiers
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The rising interest in pattern recognition and data analytics has spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm has its strengths and weaknesses, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create a high- performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers, using joint matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. Performance is evaluated on synthetic and real datasets.Peer ReviewedPostprint (author's final draft
Clustering Patients with Tensor Decomposition
In this paper we present a method for the unsupervised clustering of
high-dimensional binary data, with a special focus on electronic healthcare
records. We present a robust and efficient heuristic to face this problem using
tensor decomposition. We present the reasons why this approach is preferable
for tasks such as clustering patient records, to more commonly used
distance-based methods. We run the algorithm on two datasets of healthcare
records, obtaining clinically meaningful results.Comment: Presented at 2017 Machine Learning for Healthcare Conference (MLHC
2017). Boston, M
Blind Multiclass Ensemble Classification
The rising interest in pattern recognition and data analytics has spurred the
development of innovative machine learning algorithms and tools. However, as
each algorithm has its strengths and limitations, one is motivated to
judiciously fuse multiple algorithms in order to find the "best" performing
one, for a given dataset. Ensemble learning aims at such high-performance
meta-algorithm, by combining the outputs from multiple algorithms. The present
work introduces a blind scheme for learning from ensembles of classifiers,
using a moment matching method that leverages joint tensor and matrix
factorization. Blind refers to the combiner who has no knowledge of the
ground-truth labels that each classifier has been trained on. A rigorous
performance analysis is derived and the proposed scheme is evaluated on
synthetic and real datasets.Comment: To appear in IEEE Transactions in Signal Processin