We introduce an algorithm, called Robust Fuzzy Clustering for Multiple Instance Regression (RFC-MIR), that can learn multiple linear models simultaneously. First, RFC-MIR uses constrained fuzzy memberships to o
1. At first, each instance receives a positivity score based on clustering of data in random subspaces, which indicates the likelihood that an instance is positive. The computation of these scores is described in Section 3.1. Given these scores, an instance selection probability distribution is ...
Overall, the COMMUNAL method for mapping unsupervised clustering integrates information from multiple subsets, algorithms and validity metrics (optimized to the local data) to provide the user with a 3D map of cluster optimality. This map can assist in determining a robust choice of optimal cluster ...
variance estimation methods that explicitly account for secondary sampling. However, these methods require more assumptions than thesvyestimator. If these assumptions are correct, then these other methods may yield more efficient variance estimates. However, thesvyestimator is more robust than these ...
However, the integration of PART with robust approaches to address multi-instance complexities remains largely unexplored in the literature. To complement the strengths of PART, the SimpleMI classifier [12] serves as a fundamental component for managing the complexity of bagged instances in multi-...
while environmental and lifestyle factors are considered the major contributors to MS. For instance, although infection with Epstein–Barr virus (EBV) frequently occurs in childhood and usually is symptomless, delayed infection into early adulthood, as typically observed in countries with high standards...
Rose, K.: Deterministic annealing, constrained clustering, and optimization. In: IJCNN (1998) Gehler, P., Chapelle, O.: Deterministic annealing for multiple-instance learning. In: AISTATS (2007) Oza, N., Russell, S.: Online bagging and boosting. In: Proceedings Artificial Intelligence and Sta...
While multiple instance cluster- ing (MIC) approaches [37, 36] are designed to explore hid- den patterns in multiple classes, their performance is poor because they treat all the images as positive bags and there are no negative bags. In bMCL, we propose a maximum margin clustering concept ...
Learning paradigms can be categorized according to the task goal into supervised learning (classification for predicting a discrete label, regression for predicting a continuous output) and unsupervised learning (clustering, pattern mining, among others). However, these paradigms can be also broken down...
Hence, the data determine the size of the subset and the clustering algorithm decides on which informative genes are to be included. Since no classifier takes part during the subset construction, our subsets perform efficiently across several classification algorithms, for instance SVM-linear, KNN ...