Stable Feature Selection in Empty Spaces : Applications to Gene Profiling and Diagnosis from DNA Microarrays

Thibault Helleputte

ISBN 10: 3844390146 / ISBN 13: 9783844390148

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Many technological fields produce data characterised by a steadily growing number of dimensions. That number is however often growing much faster than the number of points available. A typical illustration of this trend is Genomics. This setting makes many machine learning applications subject to the curse of dimensionality, making difficult the estimation of robust predictive models. This book focuses on the design and application of techniques achieving both sparse feature selection and estimation of models with good classification performance in high-dimensional, empty spaces. This challenge can be successfully addressed provided that adequate inductive biases are used to mitigate the lack of extra samples. Those biases can consist either of taking many different views of the data only (ensemble methods), or of the use of external extra information, either field expert prior knowledge or other datasets about related tasks (transfer learning or multi-task learning). The proposed methods are tested over gene expression microarray datasets for diagnosis and biomarker discovery. Those datasets are typically made of few tens of samples (patients) and thousands of dimensions (genes).

Seller Inventory # 9783844390148

Bibliographic Details

• Title: Stable Feature Selection in Empty Spaces : Applications to Gene Profiling and Diagnosis from DNA Microarrays
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3844390146
• ISBN 13: 9783844390148
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 17D millimeters 220 millimeters width by 150 millimeters height by 17 millimeters depth
• Weight: 417 grams

About this title

Synopsis

Many technological fields produce data characterised by a steadily growing number of dimensions. That number is however often growing much faster than the number of points available. A typical illustration of this trend is Genomics. This setting makes many machine learning applications subject to the curse of dimensionality, making difficult the estimation of robust predictive models. This book focuses on the design and application of techniques achieving both sparse feature selection and estimation of models with good classification performance in high-dimensional, empty spaces. This challenge can be successfully addressed provided that adequate inductive biases are used to mitigate the lack of extra samples. Those biases can consist either of taking many different views of the data only (ensemble methods), or of the use of external extra information, either field expert prior knowledge or other datasets about related tasks (transfer learning or multi-task learning). The proposed methods are tested over gene expression microarray datasets for diagnosis and biomarker discovery. Those datasets are typically made of few tens of samples (patients) and thousands of dimensions (genes).

About the Author

Thibault Helleputte was born in Belgium in 1982. He graduated as an Engineer in Computing Science in 2006 at the Université catholique de Louvain, where he also received a PhD in 2010. A large part of his work is dedicated to engineering solutions for biomedical applications. http://www.thibaulthelleputte.be