Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 81 - Contributed Poster Presentations: Section on Statistics in Epidemiology
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #309913
Title: Fractal Dimension as a Dimensionality Reduction Tool in Gene Expression Analysis
Author(s): Hansapani Rodrigo* and Manoj Peiris and Myrine Barreiro-Areval and Rebecca Bernal
Companies: University of Texas Rio Grande Valley and University of Texas Rio Grande Valley and University of Texas Rio Grande Valley and University of Texas Rio Grande Valley
Keywords: Fractal Dimension; Dimensionality Reduction; Gene Expression Analysis
Abstract:

A fractal dimension helps to measure the complexity of an object by providing a statistical index of complexity as a ratio, describing on how detailed measurements can increase or decrease as we change a scale. The idea of fractal dimension can be utilized to solve dimension reduction problems associated with big data.In this work,we demonstrate the usefulness of the fractal dimension as a dimensionality reduction tool using a combination of two scalable algorithms; the box method and the fractal dimensionality reduction algorithm proposed by Traina et al. in 2000. The box method approach allows to partition a data set and compute the fractal dimension. The fractal dimensionality reduction algorithm uses the approach of backward elimination of the features of a given dataset.The underlying concept has been tested using both simulation and real world data set related to gene expression analysis. Finally, we have evaluated the impact from the dimensional reduction in increasing the model accuracy in detecting breast cancer patients using a random forest(RF) algorithm. The RF model with fractal dimension resulted with an accuracy over 75% compared to a model without feature reduction


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program