We present our system for the Interspeech 2013 Computational Paralinguistics Autism Sub-challenge. Our contribution focuses on improving classification accuracy of developmental disorders by applying a novel feature selection technique to the rich set of acoustic-prosodic features provided for this purpose. Our feature selection approach is based on submodular function optimization. We demonstr...

Short-texts accentuate the challenges posed by the high feature space dimensionality of text learning tasks. The linked nature of social data causes new dimensions to be added to the feature space, which, also becomes sparser. Thus, efficient and scalable online feature selection becomes a crucial requirement of numerous large-scale social applications. This thesis proposes an online feature se...

Feature-weight assignment can be regarded as a generalization of feature selection. That is, if all values of featureweights are either 1 or 0, feature-weight assignment degenerates to the special case of feature selection. Generally speaking, a number in 1⁄20; 1 can be assigned to a feature for indicating the importance of the feature. This paper shows that an appropriate assignment of feature...

Feature selection is a process of choosing a subset of relevant features so that the quality of prediction models can be improved. An extensive body of work exists on information-theoretic feature selection, based on maximizing Mutual Information (MI) between subsets of features and class labels. The prior methods use a lower order approximation, by treating the joint entropy as a summation of ...

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...

A margin based feature selection approach is explored for hyperspectral data. This approach is based on measuring the confidence of a classifier when making predictions on a test data. Greedy feature flip and iterative search algorithms, which attempts to maximise the margin based evaluation functions, were used in the present study. Evaluation functions use linear, zero-one and sigmoid utility...

This paper introduces ADHOC, a tool that integrates statistical methods and machine learning techniques to perform effective feature selection. Feature selection plays a central role in the data analysis process since redundant and irrelevant features often degrade the performance of induction algorithms, both in speed and predictive accuracy. ADHOC combines the advantages of both filter and fe...

The problem of studying pattern recognition techniques for analyzing textured surfaces is considered in this thesis and the results are applied to the classification of steel surfaces according to their surface properties. Various texture analysis techniques are studied and features are extracted from steel surfaces. Two new texture analysis methods are introduced and tested. To simplify and en...

Abstract   Recent developments in robotics automation have motivated researchers to improve the efficiency of interactive systems by making a natural man-machine interaction. Since speech is the most popular method of communication, recognizing human emotions from speech signal becomes a challenging research topic known as Speech Emotion Recognition (SER). In this study, we propose a Persian em...

Feature selection has been proven to be efficient in preparing high dimensional data for data mining and machine learning. As most data is unlabeled, unsupervised feature selection has attracted more and more attention in recent years. Discriminant analysis has been proven to be a powerful technique to select discriminative features for supervised feature selection. To apply discriminant analys...