Learning Classifier Performance in an Ensemble of Classifiers for Personality Prediction Using Laughter

This paper conducts a study on the acoustic features of laughter signals and performs experiments to identify the most relevant features. Different acoustic features are extracted from laughter signals, and studies are performed to identify the features that are more representative of the personality traits. An ensemble learning algorithm is proposed in this paper that gets the laughter signals from the speakers as input and predicts their personality traits. A Wagging algorithm is presented in this work that generates a set of diverse learning algorithms. A pruning algorithm is then proposed that finds the optimal subset of learning algorithms for the ensemble. In the proposed ensemble learning algorithm, a weighted averaging scheme is proposed to aggregate the output of the learning algorithms. In this scheme, during the training phase, a mechanism is adopted that measures the performance of each classifier in different areas in the feature space. These data are then used to generate a model of the performance of the basic classifiers at any given point in the feature space. In the classification phase, for any given test data record, this model is used to estimate the accuracy of the base learners. Then, a vote is performed among the classifiers, and this estimate is used to adjust the weight of the votes. The experiments are performed over a corpus of 1157 laughter bouts produced by 120 individuals to study whether laughter could be used to predict whether a person is above or below the median with respect to personality traits.