Algoritmo kNN na imputação de dados de espectros de massa do tipo MALDI-TOF: uma análise da influência da imputação com kNN sobre o desempenho de classificadores logísticos para identificação de bactérias

Abstract

It is subject of several studies in bioinformatics area the plant growth promoting bacteria identification process. An approach to performing it is to process sample’s ribosomal proteins data obtained by MALDI-TOF mass spectrometry through a classifier and select the highest probability label. However, at the time of mass spectra generation, it is common not detecting some ribosomal proteins related peaks data. With this in mind, this work presents a study about data imputation through the kNN algorithm. Logistic classifiers were applied to identify bacteria of the Bacillus genus and the Staphylococcus aureus species while three data imputation techniques were tested: with zero, with the average of the missing attribute, and with kNN algorithm. From this latter imputation technique, two approaches were considered: average aggregation function and median aggregation function. The adopted experimental protocol investigated the imputation influence on classification results under different scenarios regarding missing variablesnumber.TheresultsshowthatbothkNN’sapproachesdidnotpromotesignificantreduction on classifiers’ performance when compared with complete data approach and that the classification of imputed data by kNN presented superior performance to that of other considered methods.