J Biomed Inform - Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis.

Tópicos

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Resumo

In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce.

Resumo Limpo

last decad machin learn ml techniqu use develop classifi automat brain tumour diagnosi howev develop ml model reli uniqu train set learn stop set process train classifi requir repres amount data gather preprocess valid sampl expens timeconsum therefor classic nonincrement approach ml necessari wait long enough collect requir data contrast increment learn approach may allow us build initi classifi smaller number sampl updat increment new data collect studi increment learn algorithm gaussian discrimin analysi igda base graybil deal weight combin estim introduc time new set data becom avail new estim carri combin previous estim perform igda requir access previous use data abl includ new class origin analysi thus allow custom model distribut data particular clinic center evalu use five benchmark databas use evalu behaviour igda algorithm term stabilityplast class inclus order effect final igda algorithm appli automat brain tumour classif magnet reson spectroscopi compar two stateoftheart increment algorithm empir result obtain show abil algorithm learn increment fashion improv perform model new inform avail converg cours time furthermor algorithm show neglig instanc concept order effect avoid bias effect introduc

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