We propose an approach to multiple-instance learning that reformulates the problem as a convex optimization on the likelihood ratio between the positive and the negative class for each training instance. This is casted as joint estimation of both a likelihood ratio predictor and the target (likelihood ratio variable) for instances. Theoretically, we prove a quantitative relationship between the risk estimated under the 0-1 classification loss, and under a loss function for likelihood ratio. It is shown that likelihood ratio estimation is generally a good surrogate for the 0-1 loss, and separates positive and negative instances well. The likelihood ratio estimates provide a ranking of instances within a bag and are used as input features to learn a linear classifier on bags of instances. Instance-level classification is achieved from the bag-level predictions and the individual likelihood ratios. Experiments on synthetic and real datasets demonstrate the competitiveness of the approach.

In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances. CFAO is a generalized learning framework that contains various learning problems, such as multiple-instance learning and learning from label proportions. The goal of this paper is to present a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary losses -- previous research failed to achieve this goal. Practically, our method works by weighing the importance of each label for each instance in the group, which provides purified supervision for the classifier to learn. Theoretically, our proposed method not only guarantees the risk consistency due to the unbiased risk estimator but also can be compatible with arbitrary losses. Extensive experiments on various problems of CFAO demonstrate the superiority of our proposed method.

Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.

This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharma(cid:173) ceutical data set and on applications in automated image indexing and document categorization.

An Application of Multiple-Instance Learning to Estimate Generalization Risk Daiki Suehiro Kyushu University / RIKEN Abstract We focus on several learning approaches that employ max-operator to evaluate the margin. For example, such approaches are commonly used in multi-class learning task and top-rank learning task. In general, in order to estimate the theoretical generalization risk, we need to individually evaluate the complexity of each hypothesis class used in the learning approaches. In this paper, we provide a technique to estimate a theoretical generalization risk for such learning approaches in a same fashion. The key idea is to "redundantly" reformulate the learning problem as one-class multiple-instance learning by redefining the specific input space based on the original input space. Surprisingly, we succeed to improve the generalization risk bounds for some multi-class learning and top-rank learning algorithms. 1 Introduction A lot of margin-based learning approaches such as SVMs have a strong theoretical generalization risk bound and well works in practice. In this paper, we focus on learning approaches that define a margin based on max-operator.

In multi-instance (MI) learning, each object (bag) consists of multiple feature vectors (instances), and is most commonly regarded as a set of points in a multidimensional space. A different viewpoint is that the instances are realisations of random vectors with corresponding probability distribution, and that a bag is the distribution, not the realisations. In MI classification, each bag in the training set has a class label, but the instances are unlabelled. By introducing the probability distribution space to bag-level classification problems, dissimilarities between probability distributions (divergences) can be applied. The bag-to-bag Kullback-Leibler information is asymptotically the best classifier, but the typical sparseness of MI training sets is an obstacle. We introduce bag-to-class divergence to MI learning, emphasising the hierarchical nature of the random vectors that makes bags from the same class different. We propose two properties for bag-to-class divergences, and an additional property for sparse training sets.

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions.

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.

I propose to investigate learning in the multiple-instance (MI) framework as a problem of learning from distributions. In many MI applications, bags of instances can be thought of as samples from bag-generating distributions. Recent kernel approaches for learning from distributions have the potential to be successfully applied to these domains and other MI learning problems. Understanding when distribution-based techniques work for MI learning will lead to new theoretical insights, improved algorithms, and more accurate solutions for real-world problems.

In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent an instance may provide more significant information for learning task. Moreover, since each instance may be mapped into diverse feature spaces, encountering a large number of irrelevant or redundant features is inevitable. In this paper, we propose a method to select relevant instances and concurrently consider multiple features for each instance, which is termed as MIL-MFS. MIL-MFS is based on multiple kernel learning (MKL), and it iteratively selects the fusing multiple features for classifier training. Experimental results show that the MIL-MFS combined with multiple kernel learning can significantly improve the classification performance.