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Abstract: In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly. [Copyright &y& Elsevier]

Abstract: Objectives: Balancing the trade-offs between solution quality, problem-solving efficiency, and transparency is an important challenge in medical applications of conversational case-based reasoning (CCBR). For example, test selection in CCBR is often based on strategies in which the absence of a specific hypothesis (e.g., diagnosis) to be confirmed makes it difficult to explain the relevance of test results that users are asked to provide. In this paper, we present an approach to CCBR in medical classification and diagnosis that aims to increase transparency while also providing high levels of accuracy and efficiency. Methods: We present an algorithm for CCBR called iNN(k) in which feature selection is driven by the goal of confirming a target class and informed by a measure of a feature''s discriminating power in favor of the target class. As we demonstrate in a CCBR system called CBR-Confirm, this enables a CCBR system to explain the relevance of any question it asks the user. We evaluate the algorithm''s accuracy and efficiency on a selection of datasets related to medicine and health care. Results: The performance of iNN(k) on a given dataset is shown to depend on the value of k and on whether local or global feature selection is used in the algorithm. The combination of these parameters for which iNN(k) is most effective in addressing the trade-off between accuracy and efficiency is identified for each of the selected datasets. For example, only 42% and 51% on average of features in a complete problem description were needed by iNN(k) to provide accuracy levels of 86.5% and 84.3% respectively on the lymphography and SPECT heart datasets from the UCI machine learning repository. Conclusion: Our results demonstrate the ability of iNN(k) to provide high levels of accuracy on most of the selected datasets, while often requiring the user to provide only a small subset of the features in a complete problem description, and enabling a CCBR system to explain the relevance of any question it asks the user. [Copyright &y& Elsevier]