# A Visual Mining Approach to Improved Multiple-Instance Learning
## 2 Related Work  
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. This is carried out by choosing instance prototypes (IP) for each bag, which then can be used to learn a classifier. MILSIS [16] is also a method based on instance selection that aims to identify instance prototypes named Salient Instances, which are _true_ positive instances in positive bags.  
When visualization, data mining and machine learning are studied in the same context, it is possible to provide better tools for exploring and understanding data [24]. Studies relating these topics become even more important in the case of unstructured data since we need to obtain representations of those objects in reduced dimensions [25; 26]. There are several related studies aiming at supporting the classification process by means of visual tools [18; 19; 20; 21]. To process multidimensional data, previous visual mining approaches have employed both multidimensional projections and trees.  
Multidimensional projections map high dimensional vectors into points in a low dimensional space, such as 2D or 3D [27]. The result of this projection is a point placement on a plane, normally corresponding closely to a similarity relationship so that an instance is likely to be placed close to other similar instances and far from those that are not similar [28]. A large variety of techniques can be used to perform projection, such as Principal Component Analysis (PCA) [29], Multidimensional Scaling (MDS) [30], Least Square Projection(LSP) [31], Local Affine Multidimensional Projection (LAMP) [32], tSNE and UMAP. Although projections have improved greatly in precision and performance, they are prone to producing overlapping points, causing clutter that hampers the interaction for datasets that are reasonably large.  
On the other hand, similarity trees enforce the separability of the points by including edges between elements and causing branches between groups of similar points. They are constructed from the distances between the instances to be displayed. The Neighbor-Joining (NJ) method, originally proposed for re-constructing phylogenetic trees [22], is of particular interest. NJ builds unrooted trees, aiming at minimizing the tree length and number of tree branches by finding pairs of closed instances and creating a branch from them. In this paper, we employ an improved NJ tree layout algorithm [33] that runs faster than the original NJ [22].  
Our paper presents a novel contribution by extending the notion of visually supporting classification tasks to the case of multiple-instance learning, providing a methodology that assigns visual layouts to MIL tasks. We use a novel multi-level NJ tree, allowing users to explore MIL datasets, select the training set, create a model, visualize classification results, as well as update the current model using novel methods, as detailed in the next section.