In classifier (or regression) fusion, the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accurate and precise training labels. However, accurate labels may be difficult to obtain in many remote sensing applications. This paper proposes novel classification and regression fusion models that can be trained given ambiguously and imprecisely labeled training data in which the training labels are associated with sets of data points (i.e., “bags”) instead of individual data points (i.e., “instances”) following a multiple-instance learning framework. Experiments were conducted based on the proposed algorithms on both synthetic data and applications such as target detection and crop yield prediction given remote sensing data. The proposed algorithms show effective classification and regression performance.
The mi-SVM algorithm was proposed by @cite_45 as an MIL extension to support vector machine (SVM) learning approaches. The mi-SVM algorithm can work with bag-level labels in MIL. For two-class classification problems, the mi-SVM objective function is defined as @cite_45 : such that where @math is the weights, @math is the bias, @math is the instance-level label, @math are the slack variables (similar to that of a standard SVM). The label vector @math represents bag-leveled labels that satisfy the MIL assumption. The mi-SVM algorithm learns a linear discriminate function and maximizes the margin to separate the positive from the negative classes based on instance labels.
@cite_45
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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 heuristic ally. 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 pharmaceutical data set and on applications in automated image indexing and document categorization.