Results 281 to 290 of about 1,151,147 (311)
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Feature Selection for Clustering
2000Clustering is an important data mining task. Data mining often concerns large and high-dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high-dimensionality or both. Different features affect clusters differently, some are important for clusters while others may hinder the clustering ...
Manoranjan Dash, Huan Liu 0001
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Feature Selection for Propositionalization
2002Following the success of inductive logic programming on structurally complex but small problems, recently there has been strong interest in relational methods that scale to real-world databases. Propositionalization has already been shown to be a particularly promising approach for robustly and effectively handling larger relational data sets. However,
Mark-A. Krogel, Stefan Wrobel
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Importance Degree of Features and Feature Selection
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009A novel measure, importance degree of features, is proposed to rank the features. And a new filter method is presented to carry out feature selection based on such measure. The monotonic property of this proposed measure can reduce the search space, which results in enhancing learning efficiency.
Di Xiao, Junfeng Zhang
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2000
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-
Poggio, Tomaso A. +5 more
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We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-
Poggio, Tomaso A. +5 more
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2005
It is possible to reduce the error rate of a single classifier using a classifier ensemble. However, any gain in performance is undermined by the increased computation of performing classification several times. Here the AdaboostFS algorithm is proposed which builds on two popular areas of ensemble research: Adaboost and Ensemble Feature Selection (EFS)
D. B. Redpath, Katia Lebart
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It is possible to reduce the error rate of a single classifier using a classifier ensemble. However, any gain in performance is undermined by the increased computation of performing classification several times. Here the AdaboostFS algorithm is proposed which builds on two popular areas of ensemble research: Adaboost and Ensemble Feature Selection (EFS)
D. B. Redpath, Katia Lebart
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Regularization and feature selection for networked features
Proceedings of the 19th ACM international conference on Information and knowledge management, 2010In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, for many real world problems, we have such prior knowledge about structure relationships among features. For instance, in Microarray analysis where the genes are features, the
Hongliang Fei, Brian Quanz, Jun Huan
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Feature condensing algorithm for feature selection
2008 19th International Conference on Pattern Recognition, 2008A new unsupervised filter-based feature selection method is introduced. Its principle consists in merging similar features into clusters using a distance measure derived from the correlation coefficient. Subsequently, only one representative feature is selected from each cluster.
Pavel Krízek +2 more
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Group Feature Selection with Streaming Features
2013 IEEE 13th International Conference on Data Mining, 2013Group feature selection makes use of structural information among features to discover a meaningful subset of features. Existing group feature selection algorithms only deal with pre-given candidate feature sets and they are incapable of handling streaming features.
Hai-Guang Li +3 more
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J. Mach. Learn. Res., 2006
Summary: In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature selection offers many advantages over traditional feature selection methods, which assume that all features are known in advance.
Zhou, Jing +3 more
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Summary: In streamwise feature selection, new features are sequentially considered for addition to a predictive model. When the space of potential features is large, streamwise feature selection offers many advantages over traditional feature selection methods, which assume that all features are known in advance.
Zhou, Jing +3 more
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Ensemble Feature Selection for Rankings of Features
2015In the last few years, ensemble learning has been the focus of much attention mainly in classification tasks, based on the assumption that combining the output of multiple experts is better than the output of any single expert. This idea of ensemble learning can be adapted for feature selection, in which different feature selection algorithms act as ...
Borja Seijo-Pardo +3 more
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