Comparison of Redundancy and Relevance Measures for Feature Selection in Tissue Classification of CT Images

Auffarth, Benjamin; López, Maite; Cerquides, Jesús

doi:10.1007/978-3-642-14400-4_20

Cited by 49 publications

(37 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…methods based on the mutual information criteria such as the minimum redundancy feature selection algorithm [14]. They are usually fast but do not make use of the learning model itself.…”

Section: Related Workmentioning

confidence: 99%

Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Laubis¹,

Šimko²,

Schuller³

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…methods based on the mutual information criteria such as the minimum redundancy feature selection algorithm [14]. They are usually fast but do not make use of the learning model itself.…”

Section: Related Workmentioning

confidence: 99%

Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Laubis¹,

Šimko²,

Schuller³

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…In such a way, one variable after another is added to S . Common criteria for combining relevance and redundancy include their difference or ratio [11, 14, 15] or in a more flexible way

\begin{matrix} T1 \\ R e l (S) - γ \cdot Red (S) \end{matrix}

with a fixed γ ∈ [0,1], while choosing a fixed γ ∈ [0.5,1] was recommended by [13]. …”

Section: Mrmr Variable Selectionmentioning

confidence: 99%

“…Common examples of R 1 include measures based on mutual information [13, 14] or other approaches requiring a discretization (or even dichotomization) of the data [15], the F statistic of the analysis of variance [11], or Spearman rank correlation coefficient. Specific ad hoc measures were proposed for K = 2 and cannot be easily generalized for K > 2.…”

Section: Mrmr Variable Selectionmentioning

confidence: 99%

A Robust Supervised Variable Selection for Noisy High-Dimensional Data

Kalina

Schlenker

2015

BioMed Research International

View full text Add to dashboard Cite

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.

show abstract

“…They uses local resources. HIDS usually includes an agent fixed on every system, monitoring and informing on local OS and application action [10].…”

Section: Network-based Vs Host-based Systemmentioning

confidence: 99%