Abstract

Ensemble learning is a widely used technique in Data Mining, this method allows us to aggregate models to reduce prediction error. There are many methods on how to perform model aggregation, one of them is known as Random Subspace Learning, which consists of building subspace of the feature space where we want to create our models. The task of selecting good subspaces and in turn produce good models for better prediction can be a daunting one, so we want to propose a new method to accomplish such a task. This proposed method allows for an automated data-driven way to attribute weights to variables in the feature space in order select variables that show themselves to be important in reducing the prediction error.

Library of Congress Subject Headings

Regression analysis; Machine learning; Data mining

Publication Date

5-6-2016

Document Type

Thesis

Student Type

Graduate

Degree Name

Applied Statistics (MS)

Department, Program, or Center

School of Mathematical Sciences (COS)

Advisor

Ernest Fokoue

Advisor/Committee Member

Joseph Voelkel

Advisor/Committee Member

Steven Lalonde

Comments

Physical copy available from RIT's Wallace Library at QA278.2 .L62 2016

Campus

RIT – Main Campus

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