際際滷shows by User: pristoski / http://www.slideshare.net/images/logo.gif 際際滷shows by User: pristoski / Sun, 12 Oct 2014 09:45:59 GMT 際際滷Share feed for 際際滷shows by User: pristoski DS2014: Feature selection in hierarchical feature spaces /slideshow/ds2014-feature-selection-in-hierarchical-feature-spaces/40166382 featureselectioninhierarchicalfeaturespaces-141012094559-conversion-gate02
Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hierarchy, and further prunes the resulting feature set based on the features' relevance. We show that our method yields a good trade-off between feature space compression and classification accuracy, and outperforms both standard approaches as well as other approaches which also exploit hierarchies.]]>
Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hierarchy, and further prunes the resulting feature set based on the features' relevance. We show that our method yields a good trade-off between feature space compression and classification accuracy, and outperforms both standard approaches as well as other approaches which also exploit hierarchies.]]> Sun, 12 Oct 2014 09:45:59 GMT /slideshow/ds2014-feature-selection-in-hierarchical-feature-spaces/40166382 pristoski@slideshare.net(pristoski) DS2014: Feature selection in hierarchical feature spaces pristoski Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hierarchy, and further prunes the resulting feature set based on the features' relevance. We show that our method yields a good trade-off between feature space compression and classification accuracy, and outperforms both standard approaches as well as other approaches which also exploit hierarchies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/featureselectioninhierarchicalfeaturespaces-141012094559-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Feature selection is an important preprocessing step in data mining, which has an impact on both the runtime and the result quality of the subsequent processing steps. While there are many cases where hierarchic relations between features exist, most existing feature selection approaches are not capable of exploiting those relations. In this paper, we introduce a method for feature selection in hierarchical feature spaces. The method first eliminates redundant features along paths in the hierarchy, and further prunes the resulting feature set based on the features&#39; relevance. We show that our method yields a good trade-off between feature space compression and classification accuracy, and outperforms both standard approaches as well as other approaches which also exploit hierarchies.
DS2014: Feature selection in hierarchical feature spaces from Petar Ristoski
]]> 1152 4 https://cdn.slidesharecdn.com/ss_thumbnails/featureselectioninhierarchicalfeaturespaces-141012094559-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Comparison of Propositionalization Strategies for Creating Features from Linked Open Data /slideshow/a-comparison-of-propositionalization-strategies-for-creating-features-from-linked-open-data/39652026 acomparisonofpropositionalizationstrategiesforcreatingfeaturesfromlinkedopendata-140929083413-phpapp02
Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. In this paper, we compare different strategies for creating propositional features from Linked Open Data (a process called propositionalization), and present experiments on different tasks, i.e., classification, regression, and outlier detection. We show that the choice of the strategy can have a strong influence on the results.]]>
Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. In this paper, we compare different strategies for creating propositional features from Linked Open Data (a process called propositionalization), and present experiments on different tasks, i.e., classification, regression, and outlier detection. We show that the choice of the strategy can have a strong influence on the results.]]> Mon, 29 Sep 2014 08:34:13 GMT /slideshow/a-comparison-of-propositionalization-strategies-for-creating-features-from-linked-open-data/39652026 pristoski@slideshare.net(pristoski) A Comparison of Propositionalization Strategies for Creating Features from Linked Open Data pristoski Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. In this paper, we compare different strategies for creating propositional features from Linked Open Data (a process called propositionalization), and present experiments on different tasks, i.e., classification, regression, and outlier detection. We show that the choice of the strategy can have a strong influence on the results. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/acomparisonofpropositionalizationstrategiesforcreatingfeaturesfromlinkedopendata-140929083413-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. In this paper, we compare different strategies for creating propositional features from Linked Open Data (a process called propositionalization), and present experiments on different tasks, i.e., classification, regression, and outlier detection. We show that the choice of the strategy can have a strong influence on the results.
A Comparison of Propositionalization Strategies for Creating Features from Linked Open Data from Petar Ristoski
]]> 1081 1 https://cdn.slidesharecdn.com/ss_thumbnails/acomparisonofpropositionalizationstrategiesforcreatingfeaturesfromlinkedopendata-140929083413-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-pristoski-48x48.jpg?cb=1523668325 https://cdn.slidesharecdn.com/ss_thumbnails/featureselectioninhierarchicalfeaturespaces-141012094559-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ds2014-feature-selection-in-hierarchical-feature-spaces/40166382 DS2014: Feature select... https://cdn.slidesharecdn.com/ss_thumbnails/acomparisonofpropositionalizationstrategiesforcreatingfeaturesfromlinkedopendata-140929083413-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-comparison-of-propositionalization-strategies-for-creating-features-from-linked-open-data/39652026 A Comparison of Propos...