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Showing 1 to 20 of 400 for “"Feature selection"”.
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Hybrid Methods for Feature Selection
<p>Feature selection is one of the important data preprocessing steps in data mining. The feature selection problem involves finding a feature subset such that a classification model built only with this subset would have better predictive accuracy than model built with a complete set of features. …
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Image classification and feature selection
Made available in DSpace on 2012-06-27T21:22:52Z (GMT). No. of bitstreams: 9 Chen_Gang.pdf: 2139361 bytes, checksum: 3e14f03bb001785a473bc6193dc54f8b (MD5) license.txt: 4058 bytes, checksum: 50cddeb1b191bb1536258b22551f35da (MD5) ch4.tex: 87063 bytes, checksum: 8fac0f031e2c4b465952bb040f6131fa …
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Localized Feature Selection For Unsupervised Learning
… together and dissimilar from another group. Feature selection for unsupervised learning is a technique that chooses the best feature subset for clustering. In general, unsupervised feature selection algorithms conduct feature selection in a global sense by producing a common feature subset …
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CRAFT : ClusteR-specific Assorted Feature selecTion
… framework for clustering with cluster-specific feature selection. We derive a simplified model, CRAFT, by analyzing the asymptotic behavior of the log posterior formulations in a nonparametric MAP-based clustering setting in this framework. The model handles assorted data, i.e., both numeric and …
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A Framework for Consistency Based Feature Selection
Feature selection is an effective technique in reducing the dimensionality of features in many applications where datasets involve hundreds or thousands of features. The objective of feature selection is to find an optimal subset of relevant features such that the feature size is reduced and …
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Correlation-based feature selection for machine learning
… learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain …
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Greedy Feature Selection in Tree Kernel Spaces
… learning problems without requiring an explicit feature mapping function or deep specific domain knowledge. However, as other very high dimensional kernel families, they come with two major drawbacks: first, the computational complexity induced by the dual representation makes them unpractical …
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Feature selection with a general hybrid algorithm
The Feature Selection problem involves discovering a subset of features, such that a classifier built only with this subset would have better predictive accuracy than a classifier built from the entire set of features. A large number of algorithms have already been proposed for the feature …
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High dimensional feature selection under interactive models
… data boosts the popularity of high dimensional feature selection. High dimensional feature selection aims to select relevant features from the suspected feature space by removing redundant features. Among high feature selection studies, a large number have considered main effects only, although …
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Learning and feature selection in stereo matching
… is presented which integrates learning, feature, selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image …
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Evolutionary approaches for feature selection in biological data
… this project are: 1) to investigate and develop feature selection algorithms that incorporate various evolutionary strategies, 2) using the developed algorithms to find the “most relevant” biomarkers contained in biological datasets and 3) and evaluate the goodness of extracted feature subsets …
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Acoustic Modeling and Feature Selection for Speech Recognition
The investigation of the thesis can be divided into three parts. In the first part, a nonlinear dynamic system is proposed for formant tracking. Compared to previous formant trackers depending on least squares estimation of LPC coefficients, MUSIC (Multiple Signal Classification) and ESPRIT …
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Maximal Correlation Feature Selection and Suppression With Applications
… objectives. We examine the use of the HGR in feature selection for multi-source transfer learning learning in the fewshot setting. We then apply HGR to the problem of feature suppression via enforcing marginal and conditional independence criteria with respect to a sensitive attribute, and …
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Feature Selection Using Genetic Algorithms for Human Gait Recognition
… that gait can serve as a useful biometric feature for human identification at a distance. Here we manifest the importance of feature selection in gait recognition systems. Feature selection is an important factor which impacts the classification accuracy. This goal is achieved by discarding …
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Machine learning feature selection for tuning memory page swapping
This thesis is an exploration of the virtual memory subsystem in the modern Linux kernel. It applies machine learning to find areas where better page-out decisions can be made. Two areas of possible improvement are identified and analyzed. The first area explored arises because pages in a …
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Ensemble of Feature Selection Techniques for High Dimensional Data
… warehouses, or other information repositories. Feature selection is an important preprocessing step of data mining that helps increase the predictive performance of a model. The main aim of feature selection is to choose a subset of features with high predictive information and eliminate …
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New frontiers in population-based multi-objective feature selection
Feature selection is a persistent challenge aimed at minimizing the number of features while maximizing accuracy of classification, or any other machine learning and data mining task, by mitigating the curse of dimensionality. We frame feature selection as a multi-objective binary optimization task …
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Decision tree rule-based feature selection for imbalanced data
… When dealing with an imbalanced dataset, feature selection becomes an important issue. To address it, this work proposes a feature selection method that is based on a decision tree rule and weighted Gini index. The effectiveness of the proposed methods is verified by classifying a dataset …
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