A Review of Multi-Instance Learning Assumptions <s> Alternative Assumptions <s> Multiple instance (MI) learning is a relatively new topic in machine learning. It is concerned with supervised learning but differs from normal supervised learning in two points: (1) it has multiple instances in an example (and there is only one instance in an example in standard supervised learning), and (2) only one class label is observable for all the instances in an example (whereas each instance has its own class label in normal supervised learning). In MI learning there is a common assumption regarding the relationship between the class label of an example and the “unobservable” class labels of the instances inside it. This assumption, which is called the “MI assumption” in this thesis, states that “An example is positive if at least one of its instances is positive and negative otherwise”. In this thesis, we first categorize current MI methods into a new framework. According to our analysis, there are two main categories of MI methods, instancebased and metadata-based approaches. Then we propose a new assumption for MI learning, called the “collective assumption”. Although this assumption has been used in some previous MI methods, it has never been explicitly stated,1 and this is the first time that it is formally specified. Using this new assumption we develop new algorithms — more specifically two instance-based and one metadata-based methods. All of these methods build probabilistic models and thus implement statistical learning algorithms. The exact generative models underlying these methods are explicitly stated and illustrated so that one may clearly understand the situations 1As a matter of fact, for some of these methods, it is actually claimed that they use the standard MI assumption stated above. <s> BIB001 </s> A Review of Multi-Instance Learning Assumptions <s> Alternative Assumptions <s> We describe a generalisation of the multiple-instance learning model in which a bag's label is not based on a single instance's proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We then adapt a learning-theoretic algorithm for learning in this model and present empirical results on data from robot vision, content-based image retrieval, and protein sequence identification. <s> BIB002 </s> A Review of Multi-Instance Learning Assumptions <s> Alternative Assumptions <s> Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (multiple-instance learning via embedded instance selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty <s> BIB003 </s> A Review of Multi-Instance Learning Assumptions <s> Alternative Assumptions <s> Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influence that it has on its bag’s class label. This is a more general assumption than most existing approaches use, and thus is more widely applicable. The challenge is to accurately estimate these weights in order to make predictions at the bag level. An existing approach known as MILES is retroactively identified as an algorithm that uses instance weights for MI learning, and is evaluated using a variety of base learners on benchmark problems. New algorithms for learning instance weights for MI learning are also proposed and rigorously evaluated on both artificial and realworld datasets. The new algorithms are shown to achieve better root mean squared error rates than existing approaches on artificial data generated according to the algorithms’ underlying assumptions. Experimental results also demonstrate that the new algorithms are competitive with existing approaches on real-world problems. <s> BIB004
Due (at least in part) to the inclusion of the standard MI assumption in Dietterich et al.'s (1997) definition of MI learning, it was initially adopted ubiquitously by the fledgeling MI learning community. In more recent years, there has been a trend towards the relaxation of this strict view of MI learning BIB001 . When the standard MI assumption is relaxed, other interactions between instances and the class labels of bags are possible. We refer to such interactions as MI assumptions, since we must assume that such a relationship between bags and class labels occurs when we use a learning algorithm to build a predictive model. In order to make learning computationally feasible, it is generally necessary to reduce the hypothesis space by making use of some MI assumption. While many recent authors have (implicitly or explicitly) abandoned the standard assumption, it is often not precisely stated which new assumptions have been used BIB001 . Moreover, the literature is not in agreement on whether the relaxed version of the MI problem belongs within the umbrella of MI learning, or is a separate problem. Some authors, notably  and BIB002 , refer to the relaxed MI scenario as generalized MI, while others, such as Xu (2003), BIB003 ,  and BIB004 , include alternative MI assumptions within the multi-instance framework. In particular, Xu explicitly extends the definition of MI learning to include other assumptions. We contend that the term "multi-instance learning" should contrast directly with "singleinstance learning", and connotes any type of learning where several instances can be included within a single learning example, regardless of the assumptions used. Hence, we follow Xu, and use the term to refer to the relaxed version of MI learning as well as the standard MI scenario. We shall reserve the term "generalized MI learning" to refer to MI assumptions that are strictly more general than the standard assumption, such as those proposed by Weidmann et al. and Scott et al. 6 j. foulds and e. frank