Local Feature Selection Using Gaussian Process Regression

Local Feature Selection Using Gaussian Process Regression. Pichara, K. & Soto, A. Intelligent Data Analysis (IDA), 2014.

Paper abstract bibtex 22 downloads

Most feature selection algorithms determine a global subset of features, where all data instances are projected in order to improve classification accuracy. An attractive alternative solution is to adaptively find a local subset of features for each data instance, such that, the classification of each instance is performed according to its own selective subspace. This paper presents a novel application of Gaussian Processes that improves classification performance by learning discriminative local subsets of features for each instance in a dataset. Gaussian Processes are used to build for each available feature a function that estimates the discriminative power of the feature over all the input space. Using these functions, we are able to determine a discriminative subspace for each possible instance by locally joining the features that present the highest levels of discriminative power. New instances are then classified by using a K-NN classifier that operates in the local subspaces. Experimental results show that by using local discriminative subspaces, we are able to reach higher levels of accuracy than alternative state-of-the-art feature selection approaches.

@Article{	  pichara:etal:2014,
  author	= {K. Pichara and A. Soto},
  title		= {Local Feature Selection Using Gaussian Process
		  Regression},
  journal	= {Intelligent Data Analysis (IDA)},
  volume	= {18},
  number	= {3},
  year		= {2014},
  abstract	= {Most feature selection algorithms determine a global
		  subset of features, where all data instances are projected
		  in order to improve classification accuracy. An attractive
		  alternative solution is to adaptively find a local subset
		  of features for each data instance, such that, the
		  classification of each instance is performed according to
		  its own selective subspace. This paper presents a novel
		  application of Gaussian Processes that improves
		  classification performance by learning discriminative local
		  subsets of features for each instance in a dataset.
		  Gaussian Processes are used to build for each available
		  feature a function that estimates the discriminative power
		  of the feature over all the input space. Using these
		  functions, we are able to determine a discriminative
		  subspace for each possible instance by locally joining the
		  features that present the highest levels of discriminative
		  power. New instances are then classified by using a K-NN
		  classifier that operates in the local subspaces.
		  Experimental results show that by using local
		  discriminative subspaces, we are able to reach higher
		  levels of accuracy than alternative state-of-the-art
		  feature selection approaches. },
  url		= {http://saturno.ing.puc.cl/media/papers_alvaro/Karim-IDA-2014.pdf}
}

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