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A Deep Generative Model for Missing Data Imputation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Deep Generative Model for Missing Data Imputation./
作者:	Ghanavi, Rozhina.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	66 p.
附註:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
標題:	Electrical engineering. -
電子資源:	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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