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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
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Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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

Shengqiao Li1, E James Harner, Donald A Adjeroh

  • 1The Department of Statistics, West Virginia University, Morgantown, WV 26506, USA.

BMC Bioinformatics
|November 19, 2011
PubMed
Summary

Random KNN Feature Selection (RKNN-FS) offers a faster and more stable alternative to Random Forests for high-dimensional data, excelling in noisy or unbalanced datasets.

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • High-dimensional data modeling, especially in gene expression profiling (small n, large p problems), presents significant challenges.
  • Random Forests (RF) is a common feature selection method but suffers from instability with noisy or unbalanced data.

Purpose of the Study:

  • Introduce RKNN-FS, an innovative feature selection procedure for high-dimensional datasets.
  • Address the limitations of Random Forests in terms of stability and speed for feature selection.

Main Methods:

  • RKNN-FS utilizes Random KNN (RKNN), an ensemble of k-nearest neighbor models built on random variable subsets.
  • Variable importance is determined using a support-based criterion within the RKNN framework.
  • A two-stage backward model selection method is employed for feature selection.

Main Results:

  • RKNN-FS demonstrates effectiveness in feature selection for high-dimensional microarray data.
  • RKNN achieves comparable classification accuracy to RF without feature selection.
  • RKNN-FS significantly outperforms RF in classification accuracy and stability when feature selection is applied, especially with noisy/unbalanced data.

Conclusions:

  • RKNN-FS is proposed as a superior alternative to Random Forests for feature selection in high-dimensional classification.
  • RKNN-FS offers enhanced stability, speed, and classification performance compared to RF-FS.
  • The method is simple to implement and highly effective for large-scale datasets.