Package weka.classifiers.mi

Class Summary

Class	Description
CitationKNN	Modified version of the Citation kNN multi instance classifier. For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
MDD	Modified Diverse Density algorithm, with collective assumption. More information about DD: Oded Maron (1998).
MIBoost	MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function. For more information about Adaboost, see: Yoav Freund, Robert E.
MIDD	Re-implement the Diverse Density algorithm, changes the testing procedure. Oded Maron (1998).
MIEMDD	EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm. It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
MILR	Uses either standard or collective multi-instance assumption, but within linear regression.
MINND	Multiple-Instance Nearest Neighbour with Distribution learner. It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
MIOptimalBall	This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
MISMO	Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
MISVM	Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
MIWrapper	A simple Wrapper method for applying standard propositional learners to multi-instance data. For more information see: E.
SimpleMI	Reduces MI data into mono-instance data.