M. Scherf and W. Brauer, "Feature Selection by Means of a Feature Weighting Approach," Technische Universitt Mnchen, Munich 1997. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Fast Feature Selection Using Fractal Dimension - Traina, Jr., Traina, al. (2000) (5 citations) (Correct)
....the proposed method. Section 7 gives the conclusions of this paper. 2 Survey Numerous selection methods have been studied, including genetic algorithms; sequential feature selection algorithms such as forwards, backwards and bidirectional sequential searches; and feature weighting [Aha 1995] [Scherf 1997] [Vafaie 1993] A recent survey on attribute selection using machine learning techniques is presented in [Blum 1997] The singular value decomposition (SVD) technique provides another way of reducing the dimensionality of data bygenerating an ordered set of additional axes [Faloutsos 1996] ....
M. Scherf and W. Brauer, "Feature Selection by Means of a Feature Weighting Approach," Technische Universitt Mnchen, Munich 1997.
Feature Weighting for Nearest Neighbor by Estimation.. - Inza.. (Correct)
....the distance similarity metric among instances of the same class and increasing the distance among instances of different classes. These types of algorithms can be seen in Salzberg [27] in a Nearest Hyperrectangle framework) Aha [28] and Kira and Rendell [29] Lowe [2] and Scherf and Brauer [30] have proposed another local search mechanism as gradient descent optimization to optimize a set of continuous weights. Lowe applies the gradient descent over the distance similarity metric to optimize feature weights so as to minimize the LOOCE on the training set. Near to the basic idea of ....
M. Scherf, W. Brauer, Feature Selection by Means of a Feature Weighting Approach, Technical Report no. FKI-221-97, Forschungsberichte Kunstliche Intelligenz, Institut fur Informatik, Technische Universitat Munchen, Germany, 1997.
Correlation-based Feature Selection for Machine Learning - Hall (1999) (12 citations) (Correct)
....problems) However, RELIEF does not handle redundant features. The authors state: If most of the given features are relevant to the concept, it (RELIEF) would select most of the given features even though only a small number of them are necessary for concept description. Scherf and Brauer [SB97] describe a similar instance based approach (EUBAFES) to assigning feature weights developed independently of RELIEF. Like RELIEF, EUBAFES strives to reinforce similarities between instances of the same class while simultaneously decrease similarities between instances of different classes. A ....
M. Scherf and W. Brauer. Feature selection by means of a feature weighting approach. Technical Report FKI-221-97, Technische Universitat Munchen, 1997.
GD: A Measure based on Information Theory for.. - Lorenzo.. (1998) (Correct)
....to carry out the selection of the attributes. There is much evidence that wrapper method give good results [1, 10] However, due to its computational cost, wrapper methods can only be applied in combination with classi ers of low complexity. An intermediate approach proposed by Scherf and Brauer [21] performs the feature selection in two steps. The rst step is a lter approach whose result is a set of di erent attribute subsets and the second step is a wrapper approach over the resultant subsets in the rst step. The method proposed in this work utilizes a measure based on Information ....
M Scherf and W. Brauer. Feature selection by means of a feature weighting approach. Technical Report FKI-221-97, Institut fur Informatik, Technische Universitat Munchen, 1997.
Improving RBF Networks by the Feature Selection Approach EUBAFES - Scherf Brauer Self-citation (Scherf Brauer) (Correct)
....coincides with EUBAFES regarding the goal to reinforce similarities between instances in the same and deteriorate similarities of instances in different classes. However RELIEF does not give feature subsets but continuous feature weights and uses a different metric and optimisation technique. In [6] we compare both approaches in more detail. ....
Scherf M., Brauer W., Feature Selection by Means of a Feature Weighting Approach. Technical Report No. FKI-221-97, Forschungsberichte kunstliche Intelligenz, Institut fur Informatik, Technische Universitat Munchen (1997)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC