KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Subject: search.filters.subject.Nearest neighbor rule

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Prototype selection based on minimal consistent subset and genetic algorithms
    (2008-12-01)
    Kruatrachue, Boontee
    ;
    Hongsamart, Marut
    This paper applies the genetic algorithms to identify the minimal "consistent" prototype subset [1]. This subset can be used as a prototype which correctly recognizes the entire original prototype set. This proposed genetic algorithm tries to And the minimal consistent subset to reduce recognition time in nearest neighbor [2] classification. The main difference from other genetic algorithm (GA) approaches is the hybrid of minimal consistent set identification (MCSI) method [3] and genetic algorithm. The MCSI method provides the local optimal number of prototype while the Genetic performs the global search. The proposed hybrid algorithm has been tested on several problems and compared with the results of MCSI and other GA approach [4]. © 2008 SICE.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Minimal consistent subset selection as integer nonlinear programming problem
    (2006-12-01)
    Kangkan, Kamonnat
    ;
    Kruatrachue, Boontee
    The minimal consistent subset selection is a solution of high computational demands problem of the nearest neighbor decision system. This paper presents a new approach that aims to make the problem more clearly by stating it as a constrained optimization problem, called "integer nonlinear programming problem (INLP)". In this context, we propose method that formulates the minimal consistent subset selection problem as 0-1 integer nonlinear programming problem. We show experimental result of the minimal consistent subset of "IRIS Dataset", obtained by solving its constrained optimization model. The results obtained suggest that the approach offers exactly optimal solution of the problem. © 2006 IEEE.