14.1.3.1 Feature Selection using Search

Narendra, P.M., Fukunaga, K.,
A Branch and Bound Algorithm for Feature Subset Selection,
TC(26), No. 9, September 1977, pp. 917-922. BibRef 7709

Siedlecki, W., and Sklansky, J.,
On Automatic Feature Selection,
PRAI(2), No. 2, 1988, pp. 197-220. BibRef 8800

Siedlecki, W., and Sklansky, J.,
A Note on Genetic Algorithms for Large-Scale Feature Selection,
PRL(10), November 1989, pp. 335-347. BibRef 8911
And: A2, A1:
Large-Scale Feature Selection,
HPRCV97(Chapter I:3). (Univ California) (reprint) Discussion of best-first search in the space of feature subsets, including beam-search. BibRef

Siedlecki, W.[Wojciech], Siedlecka, K.[Kinga], Sklansky, J.[Jack],
An Overview of Mapping Techniques for Exploratory Pattern Analysis,
PR(21), No. 5, 1988, pp. 411-429.
WWW Version. BibRef 8800

Siedlecki, W.[Wojciech], Siedlecka, K.[Kinga], Sklansky, J.[Jack],
Experiments on Mapping Techniques for Exploratory Pattern Analysis,
PR(21), No. 5, 1988, pp. 431-438.
WWW Version. 0309
BibRef

Yu, B.[Bin], Yuan, B.Z.[Bao-Zong],
A more efficient branch and bound algorithm for feature selection,
PR(26), No. 6, June 1993, pp. 883-889.
WWW Version. 0401
BibRef

Kittler, J.V.,
Feature Selection and Extraction,
HPRIP86(59-83). Feature Selection. BibRef 8600

Novovicova, J., Pudil, P., Kittler, J.V.,
Divergence Based Feature-Selection for Multimodal Class Densities,
PAMI(18), No. 2, February 1996, pp. 218-223.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9602
Earlier:
Feature Selection Based on Divergence for Empirical Class Densities,
SCIA95(989-996). BibRef

Pudil, P., Novovicova, J., Choakjarernwanit, N., Kittler, J.V.,
Feature Selection Based on the Approximation of Class Densities by Finite Mixtures of Special Type,
PR(28), No. 9, September 1995, pp. 1389-1398.
WWW Version. BibRef 9509

Pudil, P., Novovicova, J., Choakjarernwanit, N., Kittler, J.V.,
An Analysis of the Max-Min Approach to Feature Selection and Ordering,
PRL(14), 1993, pp. 841-847. BibRef 9300

Pudil, P., Novovicova, J., Kittler, J.V.,
Floating Search Methods in Feature-Selection,
PRL(15), No. 11, November 1994, pp. 1119-1125. BibRef 9411

Somol, P., Pudil, P., Novovicová, J., Paclík, P.,
Adaptive floating search methods in feature selection,
PRL(20), No. 11-13, November 1999, pp. 1157-1163. 0001

PDF Version. BibRef

Pudil, P., Ferri, F.J., Novovicova, J., Kittler, J.V.,
Floating Search Methods for Feature Selection with Nonmonotonic Criterion Functions,
ICPR94(B:279-283).
IEEE DOI Link BibRef 9400

Pudil, P., Novovicová, J., Somol, P.,
Feature selection toolbox software package,
PRL(23), No. 4, February 2002, pp. 487-492.
HTML Version. 0202
BibRef

Somol, P., Pudil, P.,
Feature selection toolbox,
PR(35), No. 12, December 2002, pp. 2749-2759.
WWW Version. 0209
BibRef
Earlier:
Oscillating Search Algorithms for Feature Selection,
ICPR00(Vol II: 406-409).
IEEE DOI Link
HTML Version. 0009
BibRef

Novovicová, J.[Jana], Somol, P.[Petr], Pudil, P.[Pavel],
Oscillating Feature Subset Search Algorithm for Text Categorization,
CIARP06(578-587).
Springer DOI Link 0611
BibRef

Somol, P., Pudil, P.,
Multi-Subset Selection for Keyword Extraction and Other Prototype Search Tasks Using Feature Selection Algorithms,
ICPR06(II: 736-739).
WWW Version. 0609
BibRef

Somol, P.[Petr], Pudil, P.[Pavel], Kittler, J.V.[Josef V.],
Fast Branch & Bound Algorithms for Optimal Feature Selection,
PAMI(26), No. 7, July 2004, pp. 900-912.
IEEE Abstract. IEEE Top Reference. 0406
Predict criterion values to improve search. BibRef

Somol, P., Novovicova, J., Grim, J., Pudil, P.,
Dynamic Oscillating Search algorithm for feature selection,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

Somol, P.[Petr], Novovicová, J.[Jana], Pudil, P.[Pavel],
Flexible-Hybrid Sequential Floating Search in Statistical Feature Selection,
SSPR06(632-639).
Springer DOI Link 0608
BibRef

Chen, X.W.[Xue-Wen],
An improved branch and bound algorithm for feature selection,
PRL(24), No. 12, August 2003, pp. 1925-1933.
WWW Version. 0304
BibRef

Iannarilli, F.J., Rubin, P.A.,
Feature selection for multiclass discrimination via mixed-integer linear programming,
PAMI(25), No. 6, June 2003, pp. 779-783.
IEEE Abstract. IEEE Top Reference. 0306
Recast branch-and-bound feature selection as linear programming. BibRef

Bruzzone, L., Serpico, S.B.,
A technique for feature selection in multiclass problems,
JRS(21), No. 3, February 2000, pp. 549. 0002
BibRef

Bruzzone, L.,
An Approach to Feature Selection and Classification of Remote Sensing Images Based on the Bayes Rule for Minimum Cost,
GeoRS(38), No. 1, January 2000, pp. 429-438.
IEEE Top Reference. 0002
BibRef

Serpico, S.B., Bruzzone, L.,
A new search algorithm for feature selection in hyperspectral remote sensing images,
GeoRS(39), No. 7, July 2001, pp. 1360-1367.
IEEE Top Reference. 0108
BibRef

Kim, S.W.[Sang-Woon], Oommen, B.J.,
Enhancing prototype reduction schemes with LVQ3-type algorithms,
PR(36), No. 5, May 2003, pp. 1083-1093.
WWW Version. 0301
BibRef

Kim, S.W.[Sang-Woon], Oommen, B.J.[B. John],
On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods,
PR(37), No. 2, February 2004, pp. 227-239.
WWW Version. 0311
BibRef

Kim, S.W.[Sang-Woon], Oommen, B.J.[B. John],
On Utilizing Search Methods to Select Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers,
PAMI(27), No. 1, January 2005, pp. 136-141.
IEEE Abstract. IEEE Top Reference. 0412
PCA. Determine the dimensions of the classifier. BibRef

Kim, S.W.[Sang-Woon], Oommen, B.J.[B. John],
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods,
PAMI(27), No. 3, March 2005, pp. 455-460.
IEEE Abstract. IEEE Top Reference. 0501
BibRef

Kim, S.W.[Sang-Woon], Oommen, B.J.[B. John],
Prototype reduction schemes applicable for non-stationary data sets,
PR(39), No. 2, February 2006, pp. 209-222.
WWW Version. 0512
BibRef

Kim, S.W.[Sang-Woon], Oommen, B.J.[B. John],
On using prototype reduction schemes to optimize dissimilarity-based classification,
PR(40), No. 11, November 2007, pp. 2946-2957.
WWW Version. 0707
BibRef
Earlier:
On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype Reduction Schemes,
SSPR06(826-834).
Springer DOI Link 0608
BibRef
And:
On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes,
ICIAR06(I: 15-28).
Springer DOI Link 0610
Dissimilarity representation; Dissimilarity-based classification; Prototype reduction schemes (PRSs); Mahalanobis distances (MDs) See also On Optimizing Subclass Discriminant Analysis Using a Pre-clustering Technique. BibRef

Kim, S.W.[Sang-Woon], Oommen, B.J.[B. John],
On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures,
PR(42), No. 11, November 2009, pp. 2695-2704.
Elsevier DOI Link
WWW Version. 0907
Prototype reduction schemes (PRS),; k-nearest neighbor (k-NN) classifier; Data complexity; Class-overlapping BibRef

Kim, S.W.[Sang-Woon], Gao, J.[Jian],
A Dynamic Programming Technique for Optimizing Dissimilarity-Based Classifiers,
SSPR08(654-663).
Springer DOI Link 0812
BibRef
And:
On Using Dimensionality Reduction Schemes to Optimize Dissimilarity-Based Classifiers,
CIARP08(309-316).
Springer DOI Link 0809
BibRef

Bustos, B.[Benjamin], Navarro, G.[Gonzalo], Chávez, E.[Edgar],
Pivot selection techniques for proximity searching in metric spaces,
PRL(24), No. 14, October 2003, pp. 2357-2366.
WWW Version. 0307
BibRef

Chávez, E.[Edgar], Navarro, G.[Gonzalo],
A compact space decomposition for effective metric indexing,
PRL(26), No. 9, 1 July 2005, pp. 1363-1376.
WWW Version. 0506
BibRef

Oh, I.S.[Il-Seok], Lee, J.S.[Jin-Seon], Moon, B.R.[Byung-Ro],
Hybrid Genetic Algorithms for Feature Selection,
PAMI(26), No. 11, November 2004, pp. 1424-1437.
IEEE Abstract. IEEE Top Reference. 0410
BibRef
Earlier:
Local search-embedded genetic algorithms for feature selection,
ICPR02(II: 148-151).
IEEE DOI Link 0211
BibRef

Liu, Y.[Yi], Zheng, Y.F.[Yuan F.],
FS_SFS: A novel feature selection method for support vector machines,
PR(39), No. 7, July 2006, pp. 1333-1345.
WWW Version. 0606
Sequential forward search; Support vector machines BibRef

Nakariyakul, S.[Songyot], Casasent, D.P.[David P.],
Adaptive branch and bound algorithm for selecting optimal features,
PRL(28), No. 12, 1 September 2007, pp. 1415-1427.
WWW Version. 0707
Branch and bound algorithm; Dimensionality reduction; Feature selection; Optimal subset search BibRef

Nakariyakul, S.[Songyot], Casasent, D.P.[David P.],
An improvement on floating search algorithms for feature subset selection,
PR(42), No. 9, September 2009, pp. 1932-1940.
Elsevier DOI Link
WWW Version. 0905
Dimensionality reduction; Feature selection; Floating search methods; Weak feature replacement BibRef

Yusta, S.C.[Silvia Casado],
Different metaheuristic strategies to solve the feature selection problem,
PRL(30), No. 5, 1 April 2009, pp. 525-534.
Elsevier DOI Link
WWW Version. 0903
Feature selection; Floating search; Genetic Algorithm; GRASP; Tabu Search; Memetic Algorithm BibRef

Wang, Y.[Yong], Li, L.[Lin], Ni, J.[Jun], Huang, S.H.[Shu-Hong],
Feature selection using tabu search with long-term memories and probabilistic neural networks,
PRL(30), No. 7, 1 May 2009, pp. 661-670.
Elsevier DOI Link
WWW Version. 0904
Feature selection; Tabu Search; Probabilistic neural network; Smoothing parameter BibRef

Park, M.S.[Myoung Soo], Choi, J.Y.[Jin Young],
Theoretical analysis on feature extraction capability of class-augmented PCA,
PR(42), No. 11, November 2009, pp. 2353-2362.
Elsevier DOI Link
WWW Version. 0907
Feature extraction; CA-PCA (class-augmented principal component analysis); Class information; PCA (principal component analysis); Classification BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Number of Features, Dimensionality, Dimensionality Reduction .