P
Pedro J. García-Laencina
Researcher at United States Air Force Academy
Publications - 25
Citations - 1730
Pedro J. García-Laencina is an academic researcher from United States Air Force Academy. The author has contributed to research in topics: Missing data & Artificial neural network. The author has an hindex of 11, co-authored 25 publications receiving 1381 citations. Previous affiliations of Pedro J. García-Laencina include Universidad Politécnica de Cartagena.
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Pattern classification with missing data: a review
TL;DR: The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.
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Missing data imputation using statistical and machine learning methods in a real breast cancer problem
José M. Jerez,Ignacio Molina,Pedro J. García-Laencina,Emilio Alba,Nuria Ribelles,Miguel Martín,Leonardo Franco +6 more
TL;DR: The method based on machine learning techniques were the most suited for the imputation of missing values and led to a significant enhancement of prognosis accuracy compared to imputation methods based on statistical procedures.
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K nearest neighbours with mutual information for simultaneous classification and missing data imputation
TL;DR: This article proposes a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI), which provides a missing data estimation aimed at solving the classification task.
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Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values
TL;DR: This research work analyzes a real breast cancer dataset from Institute Portuguese of Oncology of Porto with a high percentage of unknown categorical information and constructed prediction models for breast cancer survivability using K-Nearest Neighbors, Classification Trees, Logistic Regression and Support Vector Machines.
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Efficient feature selection and linear discrimination of EEG signals
TL;DR: An efficient embedded approach for feature selection and linear discrimination of EEG signals is presented, which efficiently selects and combines the most useful features for classification with less computational requirements.