Machine learning methods for classifying normal vs. tumorous tissue with spectral data
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Inclou dades d'ús des de 2022
Cita com:
hdl:2117/16220
Tipus de documentText en actes de congrés
Data publicació2009
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Machine learning is a powerful paradigm within which to analyze 1H-MRS spectral data for the automated classi¯cation of tumor
pathologies aimed to facilitate clinical diagnosis. The high dimensionality of the involved data sets makes the discover of computational models a challenging task. In this study we apply a feature selection algorithm in order to reduce the complexity of the problem. The obtained experimental results yield a remarkable classification performance of the final induced models, both in terms of prediction accuracy and number of involved spectral frequencies. A dimensionality reduction technique that
preserves the class discrimination capabilities is used for the visualization
of the final selected frequencies, thus enhancing their interpretability.
CitacióGonzález, F.F.; Belanche, Ll. Machine learning methods for classifying normal vs. tumorous tissue with spectral data. A: Congreso Internacional de Informática y Computación. "VIII Congreso Internacional de Informática y Computación (ANIEI 2009)". Ensenada: 2009.
ISBN978-607-7854-36-4
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ANIEI_2009_final.pdf | Article principal | 188,5Kb | Visualitza/Obre |