Multi-Instance Ensemble Learning With Discriminative Bags

doi:10.1109/TSMC.2021.3125040

Multi-Instance Ensemble Learning With Discriminative Bags

2022-09-01

19

PRE

AI

DOI：10.1109/TSMC.2021.3125040

Original

Original request for help

Save

Abstract

En 中文

Multi-instance learning (MIL) is more general and challenging than traditional supervised learning in that labels are given at the bag level. The popular feature mapping approaches convert each bag into an instance in the new feature space. However, most of them hardly maintain the distinguishability of bags, and the MIL model does not support self-reinforcement. In this article, we propose the multi-instance ensemble learning with discriminative bags (ELDB) algorithm with two new techniques. The bag selection technique obtains a discriminative bag set (dBagSet) according to two parts. First, considering the space and label distribution of the data, the bag selection process is optimized through discriminative analysis to obtain the basic dBagSet. Second, with the state and action transfer strategy, a dBagSet with better distinguishability is obtained through self-reinforcement. The ensemble technique trains a series of classifiers with these dBagSets and obtains the final weighted model. The experimental results show that ELDB is superior to the state-of-the-art MIL mapping solutions.

Keywords:

Computational modeling

Transforms

Prediction algorithms

Fans

Correlation

Analytical models

Stability criteria

Distinguishability

ensemble learning

mapping

multi-instance learning (MIL)

self-reinforcement

Multi-Instance Ensemble Learning With Discriminative Bags

Abstract

Journal

Organization