Random KNN feature selection - a fast and stable alternative to Random Forests

Abstract

BackgroundSuccessfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available. Random Forests (RF) is a popular and widely used approach to feature selection for such "small n, large p problems." However, Random Forests suffers from instability, especially in the presence of noisy and/or unbalanced inputs.ResultsWe present RKNN-FS, an innovative feature selection procedure for "small n, large p problems." RKNN-FS is based on Random KNN (RKNN), a novel generalization of traditional nearest-neighbor modeling. RKNN consists of an ensemble of base k-nearest neighbor models, each constructed from a random subset of the input variables. To rank the importance of the variables, we define a criterion on the RKNN framework, using the notion of support. A two-stage backward model selection method is then developed based on this criterion. Empirical results on microarray data sets with thousands of variables and relatively few samples show that RKNN-FS is an effective feature selection approach for high-dimensional data. RKNN is similar to Random Forests in terms of classification accuracy without feature selection. However, RKNN provides much better classification accuracy than RF when each method incorporates a feature-selection step. Our results show that RKNN is significantly more stable and more robust than Random Forests for feature selection when the input data are noisy and/or unbalanced. Further, RKNN-FS is much faster than the Random Forests feature selection method (RF-FS), especially for large scale problems, involving thousands of variables and multiple classes.ConclusionsGiven the superiority of Random KNN in classification performance when compared with Random Forests, RKNN-FS's simplicity and ease of implementation, and its superiority in speed and stability, we propose RKNN-FS as a faster and more stable alternative to Random Forests in classification problems involving feature selection for high-dimensional datasets.

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

“…In Drzewiecki (2016b) nine machine learning (ML) regression algorithms were tested: Cubist (Quinlan, 1993), Random Forest (RF) (Breiman, 2001), stochastic gradient boosting of regression trees (GBM) (Friedman, 2002), k-nearest neighbors (kNN), random k-nearest neighbors (rkNN) (Li et al, 2011), Multivariate Adaptive Regression Splines (MARS) (Friedman, 1991), averaged neural networks (avNN) (Ripley, 1996), support vector machines (Smola and Schölkopf, 2004) with polynomial (SVMp) and radial (SVMr) kernels. For every study area, each of them was used to predict imperviousness for both mid 1990s and late 2000s.…”

Identification of recurrence-related genes by integrating microRNA and gene expression profiling of gastric cancer

“…Random forests (RF) is one of the most important supervised methods for feature gene selection (16)(17)(18). During the classifying process, RF returns several measures of vari-able importance.…”

Machine learning models to search relevant genetic signatures in clinical context

“…With regard to classification model selection, different algorithms have been studied for the identification of differentially expressed genes in genomic data. Classification methods such as Multilayer Perceptron (NN) [23], [24], [15], Support Vector Machines (SVM) [25], Naive Bayes (NB) [26], k-Nearest Neighbour (kNN) [27], Decision Trees (DT) [28], and RF (Random Forest) [29] have been used in recent studies. Finally, prediction assessment refers to the performance of the predictive models.…”