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Showing 1 to 8 of 8 for “"feature subset selection"”.
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Effective Features and Machine Learning Methods for Document Classification
… proposed, which aims to capture low-dimensional feature subset that facilitates improved performance in text classification. The experimental results have demonstrated the advantages and usefulness of the proposed method for text classification in high-dimensional feature space in terms of the …
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Ensemble-based Supervised Learning for Predicting Diabetes Onset
… and the accuracy of predictions through feature subset selection in order to predict diabetes onset. Data from a national health check programme (similar to NHS health check) was used. The aim is to predict diabetes onset better than other similar studies within the literature. For the …
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Semantic biometrics
… signatures, useful for identification tasks. Feature subset selection techniques are employed to compare the distinguishing ability of individual semantically described physical traits. Their identification ability is also explored, both in isolation and in the improvement of the recognition …
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Large-deviation analysis and applications Of learning tree-structured graphical models
… tree-structured graphical models and salient feature subset selection for discrimination. Graphical models have proven to be a flexible class of probabilistic models for approximating high-dimensional data. Learning the structure of such models from data is an important generic task. It is …
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Machine learning for corporate failure prediction : an empirical study of South African companies
… steps: * Defining corporate failure * Sample selection * Feature selection * Data pre-processing * Feature Subset Selection * Classifier construction * Model evaluation These steps were applied to the construction of a model, using a sample of failed companies that were listed on the JSE …
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Analytical study of computer vision-based pavement crack quantification using machine learning techniques
… was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand …
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Feature extractor stacking for cross-domain few-shot learning
… knowledge of multiple source domains into one feature extractor. This enables efficient inference but necessitates re-computation of the extractor whenever a new source domain is added. Some of these methods are also incompatible with heterogeneous source domain extractor architectures. The …
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Restricting Supervised Learning: Feature Selection and Feature Space Partition
… to solve either because of the redundant features or because of the structural complexity of the generative function. Redundant features increase the learning noise and therefore decrease the prediction performance. Additionally, a number of problems in various applications such as …