46 citations · 51 across the 6 of their papers we have counts for
6 papers
Analysis of ensemble feature selection for correlated high-dimensional RNA-Seq cancer data
Aneta Polewko-Klim, Witold R. Rudnicki
Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often perfor…
Bootstrap Bias Corrected Cross Validation applied to Super Learning
Krzysztof Mnich, Agnieszka Kitlas Golińska, Aneta Polewko-Klim +1
Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results…
MDFS - MultiDimensional Feature Selection
Radosław Piliszek, Krzysztof Mnich, Szymon Migacz +4
Identification of informative variables in an information system is often performed using simple one-dimensional filtering procedures that discard information about interactions be…
All-relevant feature selection using multidimensional filters with exhaustive search
Krzysztof Mnich, Witold R. Rudnicki
This paper describes a method for identification of the informative variables in the information system with discrete decision variables. It is targeted specifically towards discov…
The All Relevant Feature Selection using Random Forest
Miron B. Kursa, Witold R. Rudnicki
In this paper we examine the application of the random forest classifier for the all relevant feature selection problem. To this end we first examine two recently proposed all rele…
Random forest models of the retention constants in the thin layer chromatography
Miron B. Kursa, Łukasz Komsta, Witold R. Rudnicki
In the current study we examine an application of the machine learning methods to model the retention constants in the thin layer chromatography (TLC). This problem can be describe…