On the use of multiple instance learning for data classification

Abstract EN

In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag.

Whilst in a single instance learning each instance is connected with the label that contains a single feature vector.

This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework.

In single-instance learning, two datasets are applied (students' dataset and iris dataset) using Naïve Bayes Classifier (NBC), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Sequential Minimal Optimization (SMO), while SimpleMI, MIWrapper, and MIBoost in multiple instances learning.

Leave One Out Cross-Validation (LOOCV), five and ten folds Cross-Validation techniques (5-CV, 10-CV) are implemented to evaluate the classification results.

A comparison of the result of these techniques is made, several algorithms are found to be more effective for classification in the multiple instances learning.

The suitable algorithms for the students' dataset are MIBoost with MLP for LOOCV with an accuracy of 75% , whereas SimpleMI with SMO for the iris dataset is the suitable algorithm for 10-CV with an accuracy of In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag.

Whilst in a single instance learning each instance is connected with the label that contains a single feature vector.

This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework.

In single-instance learning, two datasets are applied (students' dataset and iris dataset) using Naïve Bayes Classifier (NBC), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Sequential Minimal Optimization (SMO), while SimpleMI, MIWrapper, and MIBoost in multiple instances learning.

Leave One Out Cross-Validation (LOOCV), five and ten folds Cross-Validation techniques (5-CV, 10-CV) are implemented to evaluate the classification results.

A comparison of the result of these techniques is made, several algorithms are found to be more effective for classification in the multiple instances learning.

The suitable algorithms for the students' dataset are MIBoost with MLP for LOOCV with an accuracy of 75% , whereas SimpleMI with SMO for the iris dataset is the suitable algorithm for 10-CV with an accuracy of 99.33% .