Minimal representations for translation-invariant set mappings by mathematical morphology
SIAM Journal on Applied Mathematics
Floating search methods in feature selection
Pattern Recognition Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
W-operator window design by minimization of mean conditional entropy
Pattern Analysis & Applications
Adaptive branch and bound algorithm for selecting optimal features
Pattern Recognition Letters
An improved branch & bound algorithm in feature selection
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A distance-based branch and bound feature selection algorithm
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
SFFS-MR: a floating search strategy for GRNs inference
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Inference of restricted stochastic boolean GRN's by Bayesian error and entropy based criteria
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
A pattern-oriented specification of gene network inference processes
Computers in Biology and Medicine
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This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time.