Evolutionary multiple instance boosting framework for weakly supervised learning

Multiple instance boosting (MILBoost) is a framework which uses multiple instance learning (MIL) with boosting technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced multiple boosting framework—evolutionary MILBoost (EMILBoost) which utilizes differential evolution (DE) to optimize the combination of weak classifier or weak estimator weights in the framework. A standard MIL dataset MUSK and a binary classification dataset Hastie_10_2 are used to evaluate the results. Results are presented in terms of bag and instance classification error and also confusion matrix of test data.

Historical occurrence of the Northern Fulmar (Fulmarus glacialis) on the Vysočina Region (Czech Republic)

Record of the Northern Fulmar, Fulmarus glacialis, near Dlouhá Ves, on the Vysočina Region, in Bohemia, was found in literature which is put with different data. In text be corrected inexact data and emphazise original source's a single datum “from spring 1930”.

Robustness of Interval Monge Matrices in Fuzzy Algebra

Max–min algebra (called also fuzzy algebra) is an extremal algebra with operations maximum and minimum. In this paper, we study the robustness of Monge matrices with inexact data over max–min algebra. A matrix with inexact data (also called interval matrix) is a set of matrices given by a lower bound matrix and an upper bound matrix. An interval Monge matrix is the set of all Monge matrices from an interval matrix with Monge lower and upper bound matrices. There are two possibilities to define the robustness of an interval matrix. First, the possible robustness, if there is at least one robust matrix. Second, universal robustness, if all matrices are robust in the considered set of matrices. We found necessary and sufficient conditions for universal robustness in cases when the lower bound matrix is trivial. Moreover, we proved necessary conditions for possible robustness and equivalent conditions for universal robustness in cases where the lower bound matrix is non-trivial.