Evolutionary multiple instance boosting framework for weakly supervised learning

Title

Evolutionary multiple instance boosting framework for weakly supervised learning

Creator

Bhattacharjee K.; Pant M.; Srivastava S.

Description

Multiple instance boosting (MILBoost) is a framework which uses multiple instance learning (MIL) with boosting technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced multiple boosting frameworkevolutionary MILBoost (EMILBoost) which utilizes differential evolution (DE) to optimize the combination of weak classifier or weak estimator weights in the framework. A standard MIL dataset MUSK and a binary classification dataset Hastie_10_2 are used to evaluate the results. Results are presented in terms of bag and instance classification error and also confusion matrix of test data. 2021, The Author(s).

Source

Complex and Intelligent Systems, Vol-8, No. 4, pp. 3131-3141.

Date

2022-01-01

Publisher

Springer International Publishing

Subject

Boosting; Differential evolution (DE); MILBoost; Multiple instance learning (MIL)

Coverage

Bhattacharjee K., Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Uttarakhand, 247667, India; Pant M., Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Uttarakhand, 247667, India; Srivastava S., Christ University, Uttar Pradesh, Ghaziabad, 201003, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 21994536

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s40747-021-00469-9

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133949271&doi=10.1007%2fs40747-021-00469-9&partnerID=40&md5=363a753f12de79b9e5a2055c503131d8