A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources

Ramos-Pollán, Raúl; Guevara-López, Miguel Ángel; Oliveira, Eugénio

doi:10.1007/s10916-011-9692-3

Cited by 15 publications

(10 citation statements)

References 42 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, its speciality is ANNs. Encog models are implemented with a strong consideration for efficiency; it outperforms many other machine learning Java and C # libraries [40,57,80,69]. Efficiency is critical for the ANN component of Model-Evolvability Genetic Programming (MEGP).…”

Section: Appendix a Software A1 Open Beaglementioning

confidence: 99%

Modelling Evolvability in Genetic Programming

Fowler

Banzhaf

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We develop a tree-based genetic programming system, capable of modelling evolvability during evolution through artificial neural networks (ANN) and exploiting those networks to increase the generational fitness of the system. This thesis is empirically focused; we study the effects of evolvability selection under varying conditions to demonstrate the effectiveness of evolvability selection. Evolvability is the capacity of an individual to improve its future fitness. In genetic programming (GP), we typically measure how well a program performs a given task at its current capacity only. We improve upon GP by directly selecting for evolvability. We construct a system, Sample-Evolvability Genetic Programming (SEGP), that estimates the true I thank ACENET for material support. Experiments were conducted on hardware provided by ACENET, in partnership with Compute Canada, a high performance computing (HPC) consortium for universities based in the Atlantic Provinces of Canada. ACENET is funded by the Atlantic Provinces Opportunities Agency, the

show abstract

Section: Appendix a Software A1 Open Beaglementioning

confidence: 99%

Modelling Evolvability in Genetic Programming

Fowler

Banzhaf

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Bojarczuk et al [4] uses a constrained syntax GP system with a hybrid Pitt/Michigan approach that discovers classification rules in medical data sets. An ML framework for medical classification that is scaled for grid computing is described in Ramos-Pollán et al [18].…”

Section: Related Workmentioning

confidence: 99%

Efficient training set use for blood pressure prediction in a large scale learning classifier system

Hemberg

Veeramachaneni

Dernoncourt

et al. 2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

We define a machine learning problem to forecast arterial blood pressure. Our goal is to solve this problem with a large scale learning classifier system. Because learning classifiers systems are extremely computationally intensive and this problem's eventually large training set will be very costly to execute, we address how to use less of the training set while not negatively impacting learning accuracy. Our approach is to allow competition among solutions which have not been evaluated on the entire training set. The best of these solutions are then evaluated on more of the training set while their offspring start off being evaluated on less of the training set. To keep selection fair, we divide competing solutions according to how many training examples they have been tested on.

show abstract

“…It assists healthcare professionals and doctors to analyze and predict diseases [5] and is often commonly referred to as medical engineering. Numerous machine learning algorithms have been developed to extract useful patterns from raw medical data over the years [6]. These patterns have been utilized for disease prediction using classification and clustering strategies.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Nalluri

Kannan

Manisha

et al. 2017

Journal of Healthcare Engineering

View full text Add to dashboard Cite

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

show abstract

A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources

Cited by 15 publications

References 42 publications

Modelling Evolvability in Genetic Programming

Modelling Evolvability in Genetic Programming

Efficient training set use for blood pressure prediction in a large scale learning classifier system

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Contact Info

Product

Resources

About