Performance evaluation of different machine learning algorithms in presence of outliers using gene expression data

Abstract

Classification of samples into one or more populations is one of the main objectives of gene expression data (GED) analysis. Many machine learning algorithms were employed in several studies to perform this task. However, these studies did not consider the outliers problem. GEDs are often contaminated by outliers due to several steps involve in the data generating process from hybridization of DNA samples to image analysis. Most of the algorithms produce higher false positives and lower accuracies in presence of outliers, particularly for lower number of replicates in the biological conditions. Therefore, in this paper, a comprehensive study has been carried out among five popular machine learning algorithms (SVM, RF, Naïve Bayes, k-NN and LDA) using both simulated and real gene expression datasets, in absence and presence of outliers. Three different rates of outliers (5%, 10% and 50%) and six performance indices (TPR, FPR, TNR, FNR, FDR and AUC) were considered to investigate the performance of five machine learning algorithms. Both simulated and real GED analysis results revealed that SVM produced comparatively better performance than the other four algorithms (RF, Naïve Bayes, k-NN and LDA) for both small-and-large sample sizes.
 J. bio-sci. 28: 69-80, 2020

Bridge Pier Displacement Prediction and Control in Subway Tunnel Construction

“…In various experiments that ignored the outlier problem, (Shahjaman et al, 2020) employed a variety of machine learning techniques to complete the task. The process of generating data from samples for image analysis involves several steps, which can often result in contamination by outliers, thus reducing the accuracy of most algorithms.…”

“…To evaluate the performance of the five machine learning algorithms, we considered three separate outlier rates: 5%, 10%, and 50%. The results show that RF is more suitable than the other four algorithms for randomly taken values (Shahjaman et al, 2020). The ability and viability of six machine learning techniques, including RF, to forecast vertical displacements brought on by tunnel excavation were investigated by .…”