Characterizing multiple instance datasets

06/21/2018
by   Veronika Cheplygina, et al.
8

In many pattern recognition problems, a single feature vector is not sufficient to describe an object. In multiple instance learning (MIL), objects are represented by sets (bags) of feature vectors (instances). This requires an adaptation of standard supervised classifiers in order to train and evaluate on these bags of instances. Like for supervised classification, several benchmark datasets and numerous classifiers are available for MIL. When performing a comparison of different MIL classifiers, it is important to understand the differences of the datasets, used in the comparison. Seemingly different (based on factors such as dimensionality) datasets may elicit very similar behaviour in classifiers, and vice versa. This has implications for what kind of conclusions may be drawn from the comparison results. We aim to give an overview of the variability of available benchmark datasets and some popular MIL classifiers. We use a dataset dissimilarity measure, based on the differences between the ROC-curves obtained by different classifiers, and embed this dataset dissimilarity matrix into a low-dimensional space. Our results show that conceptually similar datasets can behave very differently. We therefore recommend examining such dataset characteristics when making comparisons between existing and new MIL classifiers. The datasets are available via Figshare at <https://bit.ly/2K9iTja>.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/23/2016

Using Neural Network Formalism to Solve Multiple-Instance Problems

Many objects in the real world are difficult to describe by a single num...
research
06/24/2016

Multipartite Ranking-Selection of Low-Dimensional Instances by Supervised Projection to High-Dimensional Space

Pruning of redundant or irrelevant instances of data is a key to every s...
research
09/16/2021

Probability-driven scoring functions in combining linear classifiers

Although linear classifiers are one of the oldest methods in machine lea...
research
02/06/2014

Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vector...
research
09/26/2022

Prayatul Matrix: A Direct Comparison Approach to Evaluate Performance of Supervised Machine Learning Models

Performance comparison of supervised machine learning (ML) models are wi...
research
10/25/2017

Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of ...
research
10/03/2022

A machine learning based algorithm selection method to solve the minimum cost flow problem

The minimum cost flow problem is one of the most studied network optimiz...

Please sign up or login with your details

Forgot password? Click here to reset