Paper Info
Title
On a Minimization of Variables to Represent Sparse Multi-Valued Input Decision Functions
Abstract
A multiple-valued input decision function is a mapping f:P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> →{0,1}, where P={0,1, ..., p-1}. This paper considers the learning of such a function. That is, given the TRUE-set T ⊆ P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> and the FALSE-set F ⊆ P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , obtain a function f such that f(→a)=1 for any →a ∈ T, and f(→b)=0 for any →b ∈ F. We show a method to find a function such that f depends on the least number of variables. Applications of such functions include detection of poisonous mushrooms, hepatitis and breast cancer.
Year
DOI
Venue
2019
10.1109/ISMVL.2019.00039
2019 IEEE 49th International Symposium on Multiple-Valued Logic (ISMVL)
Keywords
Field
DocType
data mining,logic minimization,machine learning,monotone increasing function,multi-valued logic,partially defined function,support minimization
Discrete mathematics,Computer science,Decision function,Minification
Conference
ISSN
ISBN
Citations 
0195-623X
978-1-7281-0093-7
1
PageRank 
References 
Authors
0.37
10
1
Name
Order
Citations
PageRank
Tsutomu Sasao11083141.62