Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, procedure depends heavily on coefficient-based importance and only concerns control false discovery rate (FDR). To further improve its adaptivity flexibility, in this paper, we propose an error-based knockoff inference met...

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and t...

Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used d...

Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...

We present an iterative algorithm for automatic feature selection and weight tuning of target cost in the context of unit selection based audio-visual speech synthesis. We perform feature selection and weight tuning for a given unit-selection corpus to make the ranking given by the target cost function consistent with the ordering given by an objective dissimilarity measure. We explicitly perfo...

in this study, a prediction model based on support vector machines (svm) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. along with vw-svm classifier, a hybrid feature selection method was used that consisted of f-score as the filter part ...

Feature selection plays an important role in improving the classification accuracy by handling redundant or irrelevant features present in the dataset. Various soft computing based hybrid approaches like neuro-fuzzy, genetic-fuzzy, rough set-neuro etc. are proposed by researchers to perform feature selection. The existing approaches gives higher complexity and computational cost with low classi...

detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. in this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (mlp) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the pre...