MILIS: Multiple Instance Learning with Instance Selection

Abstract

Multiple-instance learning (MIL) is a paradigm in supervised learning that deals with the classification of collections of instances called bags. Each bag contains a number of instances from which features are extracted. The complexity of MIL is largely dependent on the number of instances in the training data set. Since we are usually confronted with a large instance space even for moderately sized real-world data sets applications, it is important to design efficient instance selection techniques to speed up the training process without compromising the performance. In this paper, we address the issue of instance selection in MIL. We propose MILIS, a novel MIL algorithm based on adaptive instance selection. We do this in an alternating optimisation framework by intertwining the steps of instance selection and classifier learning in an iterative manner which is guaranteed to converge. Initial instance selection is achieved by a simple yet effective kernel density estimator on the negative instances.Experimental results demonstrate the utility and efficiency of the proposed approach as compared to the state-of-the-art.

A Visual Mining Approach to Improved Multiple- Instance Learning

“…The first studies in the MIL field included the Diverse Density (DD) [3], DD with Expectation Maximization (EM-DD) [23] and MI-SVM [11]. Later, several methods were proposed using instance selection strategies, such as MILES [15], MILIS [2] and IS-MIL [12]. These methods tackle the MIL problem by converting it into regular supervised learning.…”

“…The first MIL study [3], related to drug activity, assigned a positive label to a bag that has at least one positive instance. This approach makes sense for that application but has not been successful for other datasets [2]. The state-of-the-art strategies often select a prototype instance in order to represent the bag, and there are many different heuristics that can be used to perform this task.…”

“…However, there are problems in which the target of the classification is a set of several instances, each one represented by a separate feature vector. This is the case of multiple-instance learning (MIL) [2]. In MIL, an object, called a bag, contains a set of instances.…”

“…A widely used strategy is to convert the multiple-instance problem into a classical supervised learning problem by selecting a single feature vector (instance) among the several in each bag. This instance is often called instance prototype, which is later used to represent the bag both in training and classification steps, assuming it is sufficient to represent it correctly [2,15,16]. Take, as an example, the image classification case where each image would be considered as a bag and its segmented regions (represented by separate feature vectors) as instances.…”

A Novel Classification Method for Syndrome Differentiation of Patients with AIDS

“…One kind of existing MIL methods embeds each bag into an instance space based on a representative instance set selected from the training bags and then to learn a classifier in the instance space. This kind of method mainly uses the representative instances and similar function to map bags into an instance space, which includes multiple instance learning via embedded instance selection (MILES) [36], diverse density based support vector machine (DD-SVM) [37], key instance detection (KID) [38], multiple instance learning with instance selection (MILIS) [39], multiple instance learning via disambiguation (MILD_B) [40], multiple instance learning via dominant sets (MILDS) [41], and multiple instance learning via constructive covering algorithm (MilCa) [42]. However, it is inappropriate to use these novel MIL methods to resolve the problem of AIDS syndrome differentiation.…”