Smart Comparison and Assessment of Prediction Models for Better Health Using Next Generation Data Mining

Grant number: FT110100112 | Funding period: 2012 - 2017

Completed

Abstract

Prediction models can be used to provide early warning of events, such as adverse medical outcomes. This project will develop principles for the smart management of large collections of prediction models using data mining, enabling more timely medical interventions for Australians to live healthier and longer.

Related publications (5)

Discovering outlying aspects in large datasets

NX Vinh, J Chan, S Romano, J Bailey, C Leckie, K Ramamohanarao, J Pei

2016-11-01

We address the problem of outlying aspects mining: given a query object and a reference multidimensional data set, how can we disc..

Adjusting for chance clustering comparison measures

S Romano, NX Vinh, J Bailey, K Verspoor

2016-08-01

Adjusted for chance measures are widely used to compare partitions/clusterings of the same data set. In particular, the Adjusted R..

A framework to adjust dependency measure estimates for chance

S Romano, XV Nguyen, J Bailey, K Verspoor

2016-01-01

Training Robust Models with Random Projection

XV Nguyen, S Monazam Erfani, S Paisitkriangkrai, J Bailey, C Leckie, K Ramamohanarao

2016-01-01

Regularization plays an important role in machine learning systems. We propose a novel methodology for model regularization using ..

Reconsidering Mutual Information Based Feature Selection: A Statistical Significance View

Xuan Vinh Nguyen, Jeffrey Chan, James Bailey

2014-01-01

Mutual information (MI) based approaches are a popular feature selection paradigm. Although the stated goal of Mi-based feature se..

University of Melbourne Researchers

James Bailey