Using Neural Network Formalism to Solve Multiple-Instance Problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315379" target="_blank" >RIV/68407700:21230/17:00315379 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-319-59072-1_17" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-59072-1_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-59072-1_17" target="_blank" >10.1007/978-3-319-59072-1_17</a>

Result language

angličtina

Original language name

Using Neural Network Formalism to Solve Multiple-Instance Problems

Original language description

a fixed length, whereas describing them by means of a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining in importance throughout the last years. MIL formalism assumes that each object (sample) is represented by a set (bag) of feature vectors (instances) of fixed length, where knowledge about objects (e.g., class label) is available on bag level but not necessarily on instance level. Many standard tools including supervised classifiers have been already adapted to MIL setting since the problem got formalized in the late nineties. In this work we propose a neural network (NN) based formalism that intuitively bridges the gap between MIL problem definition and the vast existing knowledge-base of standard models and classifiers. We show that the proposed NN formalism is effectively optimizable by a back-propagation algorithm and can reveal unknown patterns inside bags. Comparison to 14 types of classifiers from the prior art on a set of 20 publicly available benchmark datasets confirms the advantages and accuracy of the proposed solution.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

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

Article name in the collection

Advances in Neural Networks - ISNN 2017

ISBN

978-3-319-59071-4

ISSN

0302-9743

e-ISSN

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Number of pages

8

Pages from-to

135-142

Publisher name

Springer

Place of publication

Wien

Event location

Hokkaido

Event date

Jun 21, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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