Results 281 to 290 of about 796,511 (318)

Incremental Feature Selection

Applied Intelligence, 1998
Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches.
Liu, H., Setiono, R.
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Infinite Feature Selection

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of ...
ROFFO, GIORGIO, MELZI, SIMONE, CRISTANI, Marco   +2 more
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Swarmed Feature Selection

33rd Applied Imagery Pattern Recognition Workshop (AIPR'04), 2005
Feature selection is an important part of pattern recognition, helping to overcome the curse of dimensionality problem with classifiers, among other systems. In this work, we introduce a feature selection method using particle swarm optimization. Experiments using data of others and hyperspectral remote sensed data are used to measure the performance ...
Hiram A. Firpi, Erik D. Goodman
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Importance Degree of Features and Feature Selection

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
A 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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Feature Selection for SVMs.

2001
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., Weston, Jason, Mukherjee, Sayan, Pontil, Massimiliano, Chapelle, Olivier, Vapnik, Vladimir   +5 more
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Feature Selection for Clustering

2000
Clustering 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

2002
Following 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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Regularization and feature selection for networked features

Proceedings of the 19th ACM international conference on Information and knowledge management, 2010
In 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, 2008
A 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, Josef Kittler, Václav Hlavác   +2 more
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Group Feature Selection with Streaming Features

2013 IEEE 13th International Conference on Data Mining, 2013
Group 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, Xindong Wu 0001, Zhao Li 0007, Wei Ding 0003   +3 more
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