weka.classifiers.mi

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## Package weka.classifiers.mi

Class Summary
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.
TLD | Two-Level Distribution approach, changes the starting value of the searching algorithm, supplement the cut-off modification and check missing values. For more information see: Xin Xu (2003).
TLDSimple | A simpler version of TLD, mu random but sigma^2 fixed and estimated via data. For more information see: Xin Xu (2003).

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