Explainable Multiple Instance Learning with Instance Selection Randomized Trees

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354912" target="_blank" >RIV/68407700:21230/21:00354912 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-030-86520-7_44" target="_blank" >https://doi.org/10.1007/978-3-030-86520-7_44</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-86520-7_44" target="_blank" >10.1007/978-3-030-86520-7_44</a>

Result language

angličtina

Original language name

Explainable Multiple Instance Learning with Instance Selection Randomized Trees

Original language description

Multiple Instance Learning (MIL) aims at extracting patterns from a collection of samples, where individual samples (called bags) are represented by a group of multiple feature vectors (called instances) instead of a single feature vector. Grouping instances into bags not only helps to formulate some learning problems more naturally, it also significantly reduces label acquisition costs as only the labels for bags are needed, not for the inner instances. However, in application domains where inference transparency is demanded, such as in network security, the sample attribution requirements are often asymmetric with respect to the training/application phase. While in the training phase it is very convenient to supply labels only for bags, in the application phase it is generally not enough to just provide decisions on the bag-level because the inferred verdicts need to be explained on the level of individual instances. Unfortunately, the majority of recent MIL classifiers does not focus on this real-world need. In this paper, we address this problem and propose a new tree-based MIL classifier able to identify instances responsible for positive bag predictions. Results from an empirical evaluation on a large-scale network security dataset also show that the classifier achieves superior performance when compared with prior art methods.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-030-86519-1

ISSN

0302-9743

e-ISSN

—

Number of pages

16

Pages from-to

715-730

Publisher name

Springer Science+Business Media

Place of publication

Berlin

Event location

Bilbao

Event date

Sep 13, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000713032300044