# A Visual Mining Approach to Improved Multiple-Instance Learning
## 1 Introduction  
Many machine learning problems can be solved by standard supervised learning techniques, in which an object is represented by a single feature vector [1]. 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_. MIL was introduced in [3] to solve the problem of drug activity prediction, but many other studies have already applied this approach successfully, such as image classification [4], cancer detection via images or sequences [5, 6], text categorization [7], speaker recognition [8] and web mining [9]. Amongst the characteristics of problems that are fit to be solved by MIL approaches are those in week supervision scenarios that do not work well with standard machine learning pipelines [10]. Although multi-class classification is possible with MIL, most studies addressed only binary classification where a bag can belong either to a positive or negative class: a bag is labeled as positive if it contains at least one positive instance; otherwise, it is labeled as a negative bag. Different supervised methods have been proposed to handle the MIL problem [11, 12, 13, 14]. 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 representthe 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. Applying this type of strategy, the hypothesis would be that one region in each image-instance prototype can represent the whole image and distinguishes that \"bag\" from the others. Selecting a prototype by its relevance often needs to account for different factors such as similarity measures and the bias of the importance selection algorithm [17]. In this context, users should play a central role in defining the selection criteria and adjusting parameters for the target model. Visualization techniques have been successfully employed to help users in standard classification tasks [18; 19; 20; 21]. However, these techniques are not directly applicable to the case of multiple-instance data for two reasons: first, they do not scale well, which is a problem since the MIL dataset is often large due to the granularity of instances. Second, visualization methods are often designed to visualize all instances in the same space. An adequate visualization for this task should distinguish between bags and instances, reflecting the typical structure of an MIL dataset in the same layout. Unlike previous approaches, in this paper, we propose the use of visualization to support user intervention in the multi-instance classification pipeline. We target the tasks of instance prototype selection and training set building by the analyst, performed interactively after a preliminary solution has been obtained by an automatic procedure. The central visualization in the approach is the MILTree, a multi-scale visualization technique based on the Neighbor-Joining similarity tree [22]. In the first level, only bags are placed, reducing the amount of data to be visualized compared to laying out all instances on the visual plane. The second level projects the instances belonging to a single bag, allowing the user to explore its contents. Each bag in the first level is connected to its instances in the second level, producing an intuitive structure that facilitates data analysis.