Multiple Instance Learning

In multiple instance learning (MIL), instead of the instances, there are bags and each bag has certain number of instances. Given the bags with class labels, aim of MIL is to classify bags with potentially unlabelled instances. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all the remaining instances in the data are negative. Due to the restrictiveness of this assumption, generalized MIL assumptions are also introduced to increase applicability to various MIL problems.


Common MIL datasets:

MIL algorithms are tested on 71 MIL benchmark datasets. This is the largest experimented MIL repository for algorithm comparison. Application areas of the datasets are molecular activity prediction, image annotation, text categorization, webpage classification and audio-recording classification (.mat files of the datasets are provided on

Each dataset file is a comma-separated value (CSV) formatted file which has number of instances many rows and number of features many columns together with two additionally attached columns. The first attached column corresponds to the bag class labels which are propagated to the instances. The second column is the bag ID column where each instance receives the bag ID number of its owner bag. The remaining columns individually store the feature values of the instances.

Full table of the datasets: [Table 1. MIL_datasets]

Link for the datasets: [Real_world_datasets]

PASCAL VOC 2007 dataset in MIL format:

The original natural image classification and object detection dataset can be downloaded from the original source page:

This dataset formed as a MIL problem by Dr. Melih Kandemir and Manuel Haussmann.  Their corresponding paper for citation is:

M. Haußmann, F.A. Hamprecht, M. Kandemir, “Variational Bayesian multiple instance learning with Gaussian processes”, CVPR, (2017).

Link for the PASCAL VOC 2007 MIL dataset:  [pvoc_2007_dataset]

Synthetic datasets:

These datasets are randomly generated based on four different MI-settings and can be used in MIL algorithms to measure the effects of different levels of number of bags, average number of instances per bag and number of features.

Link for the datasets: [Synthetic_datasets]

Pseudo-synthetic datasets:

Based on Elephant dataset, datasets with different levels of number of bags and number of features are generated to test MIL algorithms.

Link for the datasets: [Pseudo-synthetic_datasets]


Bag encoding for MIL:

To learn a classifier at bag-level, bags can be encoded by using their instance frequencies in specific regions of the data space. Several encoding algorithms are developed to represent bags in MIL. Then, bags are classified by using random forests.

Bag encoding algorithms to perform multiple instance classification and their corresponding results on famous MIL datasets are introduced in:

Emel Seyma Kucukasci and Mustafa Gokce Baydogan, “Bag Encoding Strategies in Multiple Instance Learning Problems,” Information Sciences 467 (2018): 559-578.

Link for the R codes: [R_codes]

Link for the Python codes: [Python_codes]

Linear programming-based MIL:

An optimization-based MIL framework is proposed to model instance level contributions to assess bag labels. Linear programming (LP) models are efficiently solved to obtain a simple linear mapping of instance-level information to assess bag-level estimates. This kind of mapping handles generalized MIL assumptions and also the standard MIL assumption. Besides, it is possible to solve LP models on different data representations.

LP-MIL method with two different data representations and the competitive performance with the state-of-the-art algorithms on a large database of MIL problems are reported in:

Emel Seyma Kucukasci, Mustafa Gokce Baydogan, and Z. Caner Taskin, “A Linear Programming Approach to Multiple Instance Learning,” Turkish Journal of Electrical Engineering & Computer Sciences 29 (2021): 2186 – 2201.

Link for the Python codes: [Python_codes]

Quadratic programming-based MIL:

We present an efficient quadratic programming (QP) model and optimization algorithm for MIL. Our QP-based approach to MIL aggregates instance-level estimations to obtain a bag label estimation score, and provides a bag-level class decision threshold. The proposed MIL framework is efficient in terms of solution time, overcoming the computational difficulties in previous MIL formulations.

We present the classification results of QP-MIL and recent MIL algorithms in the literature along with model comparisons in:

Emel Seyma Kucukasci, Mustafa Gokce Baydogan, and Z. Caner Taskin, “Multiple Instance Classification via Quadratic Programming,” Journal of Global Optimization,  accepted, 2021.

Link for the Python codes: [Python_codes]

Other MIL methods:

Standard MIL methods and bag dissimilarity-based methods (MInD) are contained in MIL Toolbox (can be downloaded from “Pattern Recognition Laboratory”s website).

Link for MIL toolbox: [Matlab_codes]

Another bag representation algorithm of MIL based on Fisher vectors (miFV) can be dowloaded from the link:  [miFV_code]


The proposed MIL approaches and the state-of-the-art methods are tested by repeating a ten-fold cross validation for five times. Randomly generated cross validation indices of the experimented datasets below can be used to reproduce the experimental results to make comparisons.

Real world datasets: [CV_indices]