Biju Patnaik Central Library

Ensemble Methods in Machine Learning -- Experiments with Classifier Combining Rules -- The “Test and Select” Approach to Ensemble Combination -- A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR -- Multiple Classifier Combination Methodologies for Different Output Levels -- A Mathematically Rigorous Foundation for Supervised Learning -- Classifier Combinations: Implementations and Theoretical Issues -- Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification -- Complexity of Classification Problems and Comparative Advantages of Combined Classifiers -- Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems -- Combining Fisher Linear Discriminants for Dissimilarity Representations -- A Learning Method of Feature Selection for Rough Classification -- Analysis of a Fusion Method for Combining Marginal Classifiers -- A hybrid projection based and radial basis function architecture -- Combining Multiple Classifiers in Probabilistic Neural Networks -- Supervised Classifier Combination through Generalized Additive Multi-model -- Dynamic Classifier Selection -- Boosting in Linear Discriminant Analysis -- Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination -- Applying Boosting to Similarity Literals for Time Series Classification -- Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS -- A New Evaluation Method for Expert Combination in Multi-expert System Designing -- Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems -- Self-Organizing Decomposition of Functions -- Classifier Instability and Partitioning -- A Hierarchical Multiclassifier System for Hyperspectral Data Analysis -- Consensus Based Classification of Multisource Remote Sensing Data -- Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps -- A Multiple Self-Organizing Map Scheme for Remote Sensing Classification -- Use of Lexicon Density in Evaluating Word Recognizers -- A Multi-expert System for Dynamic Signature Verification -- A Cascaded Multiple Expert System for Verification -- Architecture for Classifier Combination Using Entropy Measures -- Combining Fingerprint Classifiers -- Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework -- A Modular Neuro-Fuzzy Network for Musical Instruments Classification -- Classifier Combination for Grammar-Guided Sentence Recognition -- Shape Matching and Extraction by an Array of Figure-and-Ground Classifiers.

Many theoretical and experimental studies have shown that a multiple classi?er system is an e?ective technique for reducing prediction errors [9,10,11,20,19]. These studies identify mainly three elements that characterize a set of cl- si?ers: –Therepresentationoftheinput(whateachindividualclassi?erreceivesby wayofinput). –Thearchitectureoftheindividualclassi?ers(algorithmsandparametri- tion). – The way to cause these classi?ers to take a decision together. Itcanbeassumedthatacombinationmethodise?cientifeachindividualcl- si?ermakeserrors‘inadi?erentway’,sothatitcanbeexpectedthatmostofthe classi?ers can correct the mistakes that an individual one does [1,19]. The term ‘weak classi?ers’ refers to classi?ers whose capacity has been reduced in some way so as to increase their prediction diversity. Either their internal architecture issimple(e.g.,theyusemono-layerperceptronsinsteadofmoresophisticated neural networks), or they are prevented from using all the information available. Sinceeachclassi?erseesdi?erentsectionsofthelearningset,theerrorcorre- tion among them is reduced. It has been shown that the majority vote is the beststrategyiftheerrorsamongtheclassi?ersarenotcorrelated.Moreover, in real applications, the majority vote also appears to be as e?cient as more sophisticated decision rules [2,13]. Onemethodofgeneratingadiversesetofclassi?ersistoupsetsomeaspect ofthetraininginputofwhichtheclassi?erisrather unstable. In the present paper,westudytwodistinctwaystocreatesuchweakenedclassi?ers;i.e.learning set resampling (using the ‘Bagging’ approach [5]), and random feature subset selection (using ‘MFS’, a Multiple Feature Subsets approach [3]). Other recent and similar techniques are not discussed here but are also based on modi?cations to the training and/or the feature set [7,8,12,21].