Open AccessFeature Selection using Simulated Annealing with Optimal Neighborhood Approach
Author(s) -
Achmad Syaiful,
Bagus Sartono,
FM Afendi,
Rahma Anisa,
Agus Salim
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1752/1/012030
Subject(s) - akaike information criterion , simulated annealing , bayesian information criterion , feature selection , goodness of fit , mathematics , mathematical optimization , function (biology) , computer science , algorithm , statistics , artificial intelligence , evolutionary biology , biology
The one of the metaheuristic approaches that can be used was simulated annealing (SA) algorithm which inspired by annealing metallurgical process. This algorithm shows advantages in finding global optimum of given function which will be used in feature selection. In this study, we will trying to combine the neighborhood size and limited approach by using data simulation comparing between two function which is Akaike Index Criterion (AIC) function and Bayesian Index Criterion (BIC) function. The result of this experiment shows that the selected variables using optimal neighborhood size and limit the selected variable provide the result of goodness model around 98% of accuracy and specificity and 94% of sensitivity compared with simulated annealing algorithms without any modification using both AIC function and BIC function, and in the simulation also shows that BIC function give better result than AIC function.