Gavin Brown

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Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection

Gavin Brown, Adam Craig Pocock, Ming-Jie Zhao, Mikel Luján
Journal of Machine Learning Research
2012

Criterion Full name Author MI Mutual Information Maximisation Various (1970s ) MIFS Mutual Information Feature Selection Battiti (1994) JMI Joint Mutual Information Yang & Moody (1999) MIFS-U… (More)

Diversity creation methods: a survey and categorisation

Gavin Brown, Jeremy L. Wyatt, Rachel Harris, Xin Yao
Information Fusion
2004

Ensemble approaches to classification and regression have attracted a great deal of interest in recent years. These methods can be shown both theoretically and empirically to outperform single… (More)

Managing Diversity in Regression Ensembles

Gavin Brown, Jeremy L. Wyatt, Peter Tiño
Journal of Machine Learning Research
2005

We describe the results of a study on a heuristic technique that claimed to effectively balance diversity against individual accuracy between members of a neural network regression ensemble. We… (More)

Is feature selection secure against training data poisoning?

Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli
ICML
2015

Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven… (More)

"Good" and "Bad" Diversity in Majority Vote Ensembles

Although diversity in classifier ensembles is desirable, its relationship with the ensemble accuracy is not straightforward. Here we derive a decomposition of the majority vote error into three… (More)

Diversity in neural network ensembles

Gavin Brown
2004

We study the issue of error diversity in ensembles of neural networks. In ensembles of regression estimators, the measurement of diversity can be formalised as the Bias-VarianceCovariance… (More)

Toward a more accurate understanding of the limits of the TLS execution paradigm

Nikolas Ioannou, Jeremy Singer, +7 authors Marcelo Cintra
IEEE International Symposium on Workload…
2010

Thread-Level Speculation (TLS) facilitates the extraction of parallel threads from sequential applications. Most prior work has focused on developing the compiler and architecture for this execution… (More)

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

Robi Polikar, Joseph DePasquale, Hussein Syed Mohammed, Gavin Brown, Ludmila I. Kuncheva
Pattern Recognition
2010

We introduce Learn.MF, an ensemble-of-classifiers based algorithm that employs random subspace selection to address the missing feature problem in supervised classification. Unlike most established… (More)

Fundamental Nano-Patterns to Characterize and Classify Java Methods

Jeremy Singer, Gavin Brown, Mikel Luján, Adam Craig Pocock, Paraskevas Yiapanis
Electr. Notes Theor. Comput. Sci.
2010

Fundamental nano-patterns are simple, static, binary properties of Java methods, such as ObjectCreator and Recursive. We present a provisional catalogue of 17 such nano-patterns. We report… (More)

An Information Theoretic Perspective on Multiple Classifier Systems

Gavin Brown
MCS
2009

This paper examines the benefits that information theory can bring to the study of multiple classifier systems. We discuss relationships between the mutual information and the classification error of… (More)

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