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A Deep Generative Model for Missing Data Imputation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Deep Generative Model for Missing Data Imputation./
Author:	Ghanavi, Rozhina.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	66 p.
Notes:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315582
ISBN:	9798522943226

A Deep Generative Model for Missing Data Imputation.
Ghanavi, Rozhina.

A Deep Generative Model for Missing Data Imputation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 66 p.

Source: Masters Abstracts International, Volume: 83-01.

Thesis (M.A.S.)--University of Toronto (Canada), 2021.

This item is not available from ProQuest Dissertations & Theses.

Machine learning relies on data. However, real-world datasets are far from perfect. One of the biggest challenges in working with these datasets is missing data. In this work, we present a novel deep generative model for missing data imputation. What makes our method unique is the focus it puts on classification accuracy while it imputes missing data. This makes our model particularly useful for classification problems. We formulate our proposal as a sequential game and show that it learns the true data distribution. Furthermore, we propose a new algorithm for optimizing our objective. Our proposal is able to learn the feature importance and impute critical features more accurately. Experimental results show our method outperforms existing methods in terms of classification accuracy.

ISBN: 9798522943226Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Computer Networks

A Deep Generative Model for Missing Data Imputation.
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245 1 0 $a A Deep Generative Model for Missing Data Imputation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 66 p.
500 $a Source: Masters Abstracts International, Volume: 83-01.
500 $a Advisor: Liang, Ben.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Machine learning relies on data. However, real-world datasets are far from perfect. One of the biggest challenges in working with these datasets is missing data. In this work, we present a novel deep generative model for missing data imputation. What makes our method unique is the focus it puts on classification accuracy while it imputes missing data. This makes our model particularly useful for classification problems. We formulate our proposal as a sequential game and show that it learns the true data distribution. Furthermore, we propose a new algorithm for optimizing our objective. Our proposal is able to learn the feature importance and impute critical features more accurately. Experimental results show our method outperforms existing methods in terms of classification accuracy.
590 $a School code: 0779.
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650 4 $a Computer science. $3 523869
650 4 $a Computer engineering. $3 621879
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650 4 $a Artificial intelligence. $3 516317
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650 4 $a Software. $2 gtt. $3 619355
650 4 $a Accuracy. $3 3559958
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650 4 $a Classification. $3 595585
650 4 $a Algorithms. $3 536374
653 $a Computer Networks
653 $a Deep Learning
653 $a GANs
653 $a Generative Models
653 $a Machine Learning
653 $a Missing Data
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690 $a 0464
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710 2 $a University of Toronto (Canada). $b Electrical and Computer Engineering. $3 2096349
773 0 $t Masters Abstracts International $g 83-01.
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793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315582