Meta-classifier approach to reliable text classification(5)

A problem with automatic classifiers is that there is no way to know if a particular classification is just a guess or a certain answer. Reliable classification is the task of predicting whether a certain instance is correctly classified or not, i.e., a cl

Contents

Abstract

Preface

1Introduction

1.1TaskofReliableClassi cation....................

1.2RelatedWork.............................

1.2.1BayesianFramework.....................

1.2.2StatisticalFrameworks....................

1.2.3Version-SpaceSupport-VectorMachines..........

1.2.4Meta-Classi erApproach..................

1.3ProblemStatementandResearchQuestions............

1.4ThesisOutline............................

2TheCBSData

2.1Backgrond...............................

2.2DataDescription...........................

2.3DataPreparation...........................

2.3.1SelectionofInformativeFields...............

2.3.2Feature-Reduction......................

2.4ChapterSummary..........................

3TextClassi cation

3.1TextRepresentation.........................

3.2Classi cationAlgorithms......................

3.2.1Na¨ veBayes..........................

3.2.2NearestNeighbour......................

3.2.3HierarchicalClassi er....................

3.3ExperimentswiththeTextClassi ers...............

3.3.1ExperimentalSettingandMethodology..........

3.3.2ExperimentalResults....................

3.3.3EvaluationandDiscussionoftheResults.........

3.4ChapterSummary..........................

4Meta-Classi erApproachestoReliableClassi cation

4.1De nition...............................

4.2MetadataRepresentations......................

4.3GenerationofMetadata.......................

iviiiii11222344677899911121213141414151516192122222324