Adaptive branch and bound algorithm for selecting optimal features

Nakariyakul, Songyot; Casasent, David P.

doi:10.1016/j.patrec.2007.02.015

Cited by 53 publications

(31 citation statements)

References 11 publications

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“…Devijver et al (1982)) is available, the only tool needed is the search algorithm that generates a sequence of subsets to be tested. Despite the advances in optimal search (Somol et al (2004), Nakariyakul et al (2007)), for larger than moderate-sized problems we have to resort to sub-optimal methods. Very large number of various methods exists.…”

Section: Sub-optimal Search Methodsmentioning

confidence: 99%

Efficient Feature Subset Selection and Subset Size Optimization

Somol¹,

Novovičová²,

Pudil³

2010

Pattern Recognition Recent Advances

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Section: Sub-optimal Search Methodsmentioning

confidence: 99%

Efficient Feature Subset Selection and Subset Size Optimization

Somol¹,

Novovičová²,

Pudil³

2010

Pattern Recognition Recent Advances

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“…Branch and bound algorithms [10,31,32,35,37] are capable of computing the best subset of features according to the criterion functions used in these studies, such as the Bhattacharyya distance and divergence. These algorithms make an efficient computation by assuming that a subset of features should be not better than any larger sets containing it.…”

Section: Introductionmentioning

confidence: 99%

Feature subset selection for logistic regression via mixed integer optimization

et al. 2016

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This paper concerns a method of selecting a subset of features for a logistic regression model. Information criteria, such as the Akaike information criterion and Bayesian information criterion, are employed as a goodness-offit measure. The feature subset selection problem is formulated as a mixed integer linear optimization problem, which can be solved with standard mathematical optimization software, by using a piecewise linear approximation. Computational experiments show that, in terms of solution quality, the proposed method has superiority over common stepwise methods.

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“…Numerous approaches have been applied to feature selection, e.g. generic algorithms (Van Coillie et al, 2007;da Silva et al, 2009;Lee et al, 2006), artificial neural network (ANN) (Saravanan and Ramachandran, 2010;Chakrabarti, 2008;Glezakos et al, 2009), Tabu Theory (Wang et al, 2010;Wang et al, 2009), branch-and-bound algorithm (Nakariyakul and Casasent, 2007;Chen, 2003), Fuzzy C-means Algorithm (Francesco, 2003) and SOM (Laaksonen et al, 2004). However, these approaches are not used for processing qualitative information.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction using rough set theory in service sector application from incremental perspective

Huang

Tseng

Tang

2016

Computers & Industrial Engineering

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In service industry application, there is vague and qualitative information required to be processed properly, for example, to identify customer preferences in order to provide adequate services. From literature, Rough Set Theory (RST) has been indicated to be one of promising approaches to cope with vagueness in a large scale database. Basically, the rough set approach integrates learning-from-example techniques, extracts rules from a data set of interest, and discovers data regularities. Most of the existing RS based approaches are able to implement rule induction but it is very time consuming from computation perspective particularly from a large database. To date, there is a demand to generate and analyze business decision rules based on dynamical data sets and conclude such rules on the daily basis in the service industry. Therefore, in this study, an Incremental Weight Incorporated Rule Identification (IWIRI) algorithm is proposed to fulfill such demand. The proposed approach is proficient to efficiently process in-coming data (objetcs) and generate updated decision rules without re-computation efforts in the database. Identification of features based on the customer's preference and implementation of the proposed algorithm are summarized in the case study. This paper forms the basis for solving many other similar problems that occur in service industries.

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Adaptive branch and bound algorithm for selecting optimal features

Cited by 53 publications

References 11 publications

Efficient Feature Subset Selection and Subset Size Optimization

Efficient Feature Subset Selection and Subset Size Optimization

Feature subset selection for logistic regression via mixed integer optimization

Feature extraction using rough set theory in service sector application from incremental perspective

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