Robust Dimensionality Reduction: A Resistant Search for the Relevant Information in Complex Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00565535" target="_blank" >RIV/67985807:_____/23:00565535 - isvavai.cz</a>

Výsledek na webu

<a href="https://dx.doi.org/10.4018/978-1-6684-5092-5.ch004" target="_blank" >https://dx.doi.org/10.4018/978-1-6684-5092-5.ch004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-6684-5264-6.ch009" target="_blank" >10.4018/978-1-6684-5264-6.ch009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Dimensionality Reduction: A Resistant Search for the Relevant Information in Complex Data

Popis výsledku v původním jazyce

With the increasing availability of massive data in various fields of applications such as engineering, economics, or biomedicine, there appears an urgent need for new reliable tools for obtaining relevant knowledge from such data, which allow one to find and interpret the most relevant features (variables). Such interpretation is however infeasible for the habitually used methods of machine learning, which can be characterized as black boxes. This chapter is devoted to variable selection methods for finding the most relevant variables for the given task. After explaining general principles, attention is paid to robust approaches, which are suitable for data contaminated by outlying values (outliers). Three main approaches to variable selection (prior, intrinsic, and posterior) are explained, and their recently proposed examples are illustrated on applications related to credit risk management and molecular genetics. These examples reveal recent robust approaches to data analysis to be able to outperform non-robust tools.

Název v anglickém jazyce

Robust Dimensionality Reduction: A Resistant Search for the Relevant Information in Complex Data

Popis výsledku anglicky

With the increasing availability of massive data in various fields of applications such as engineering, economics, or biomedicine, there appears an urgent need for new reliable tools for obtaining relevant knowledge from such data, which allow one to find and interpret the most relevant features (variables). Such interpretation is however infeasible for the habitually used methods of machine learning, which can be characterized as black boxes. This chapter is devoted to variable selection methods for finding the most relevant variables for the given task. After explaining general principles, attention is paid to robust approaches, which are suitable for data contaminated by outlying values (outliers). Three main approaches to variable selection (prior, intrinsic, and posterior) are explained, and their recently proposed examples are illustrated on applications related to credit risk management and molecular genetics. These examples reveal recent robust approaches to data analysis to be able to outperform non-robust tools.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Aproximativní neurovýpočty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Convergence of Big Data Technologies and Computational Intelligent Techniques

ISBN

9781668452646

Počet stran výsledku

25

Strana od-do

186-210

Počet stran knihy

233

Název nakladatele

IGI Global

Místo vydání

Hershey

Kód UT WoS kapitoly

—