rmcfs: An R Package for Monte Carlo Feature Selection and Interdependency Discovery
نویسندگان
چکیده
منابع مشابه
Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification
Applications of machine learning techniques in Life Sciences are the main applications forcing a paradigm shift in the way these techniques are used. Rather than obtaining the best possible supervised classifier, the Life Scientist needs to know which features contribute best to classifying observations into distinct classes and what are the interdependencies between the features. To this end w...
متن کاملMonte Carlo feature selection for supervised classification
MOTIVATION Pre-selection of informative features for supervised classification is a crucial, albeit delicate, task. It is desirable that feature selection provides the features that contribute most to the classification task per se and which should therefore be used by any classifier later used to produce classification rules. In this article, a conceptually simple but computer-intensive approa...
متن کاملmRMRe: an R package for parallelized mRMR ensemble feature selection
MOTIVATION Feature selection is one of the main challenges in analyzing high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a particularly fast feature selection method for finding a set of both relevant and complementary features. Here we describe the mRMRe R package, in which the mRMR technique is extended by using an ensemble approach to better explore the feature sp...
متن کاملrlsm: R package for least squares Monte Carlo
This short paper briefly describes the implementation of the least squares Monte Carlo method in the rlsm package. This package provides users with an easy manner to experiment with the large amount of R regression tools on any regression basis and reward functions. This package also computes lower and upper bounds for the true value function via duality methods.
متن کاملSelection of Significant Features Using Monte Carlo Feature Selection
Feature selection methods identify subsets of features in large datasets. Such methods have become popular in data-intensive areas, and performing feature selection prior tomodel constructionmay reduce the computational cost and improve the model quality. Monte Carlo Feature Selection (MCFS) is a feature selection method aimed at finding features to use for classification. Here we suggest a str...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2018
ISSN: 1548-7660
DOI: 10.18637/jss.v085.i12