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A weight-adjusted voting algorithm for ensembles of classifiers
김현중,김혁,Hojin Moon,Hongshik Ahn 한국통계학회 2011 Journal of the Korean Statistical Society Vol.40 No.4
We present a new weighted voting classification ensemble method, called WAVE, that uses two weight vectors: a weight vector of classifiers and a weight vector of instances. The instance weight vector assigns higher weights to observations that are hard to classify. The weight vector of classifiers puts larger weights on classifiers that perform better on hardto-classify instances. One weight vector is designed to be calculated in conjunction with the other through an iterative procedure. That is, the instances of higher weights play a more important role in determining the weights of classifiers, and vice versa. We proved that the iterated weight vectors converge to the optimal weights which can be directly calculated from the performance matrix of classifiers in an ensemble. The final prediction of the ensemble is obtained by voting using the optimal weight vector of classifiers. To compare the performance between a simple majority voting and the proposed weighted voting, we applied both of the voting methods to bootstrap aggregation and investigated the performance on 28 datasets. The result shows that the proposed weighted voting performs significantly better than the simple majority voting in general.
Walsh-Messinger, Julie,Jiang, Haoran,Lee, Hyejoo,Rothman, Karen,Ahn, Hongshik,Malaspina, Dolores Elsevier/North Holland Biomedical Press 2019 Psychiatry Research Vol. No.
<P><B>Abstract</B></P> <P>This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (<I>n</I> = 60), schizoaffective disorder (<I>n</I> = 19), bipolar disorder (<I>n</I> = 20), unipolar depression (<I>n</I> = 14), and healthy controls (<I>n</I> = 51) into psychiatric diagnostic categories. The Random Forest machine learning algorithm, which showed best efficacy (92.9% SD: 0.06), was used to generate variable importance ranking of positive, negative, and general psychopathology symptoms, cognitive indexes, global assessment of function (GAF), and parental ages at birth for sorting participants into diagnostic categories. Symptoms were ranked most influential for separating cases from healthy controls, followed by cognition and maternal age. To separate schizophrenia/schizoaffective disorder from bipolar/unipolar depression, GAF was most influential, followed by cognition and paternal age. For classifying schizophrenia from all other psychiatric disorders, low GAF and paternal age were similarly important, followed by cognition, psychopathology and maternal age. Controls misclassified as schizophrenia cases showed lower nonverbal abilities, mild negative and general psychopathology symptoms, and younger maternal or older paternal age. The importance of symptoms for classification of cases and lower GAF for diagnosing schizophrenia, notably more important and distinct from cognition and symptoms, concurs with current practices. The high importance of parental ages is noteworthy and merits further study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Machine-learning algorithms estimated importance of multilevel data for diagnostic classification. </LI> <LI> Symptoms were most influential for differentiating psychiatric cases from healthy controls. </LI> <LI> Function was most important for separating the schizophrenias from affective disorder cases. </LI> <LI> Function and paternal age were equally important for separating schizophrenia from all other cases. </LI> <LI> Misclassified controls had mild symptoms, lower cognition, and/or younger mothers/older fathers. </LI> </UL> </P>