Quick online feature selection method for regression -A feature selection method inspired by human behavior-
Youhei Tadeuchi, Ryuji Oshima, Kyosuke Nishida, Koichiro Yamauchi, Takashi Omori · 2007
The task of variable selection is essential to improving the ability of machine learning systems to generalize. Although there are many conventional variable selection methods, almost all of them need to prepare and learn a large number of samples in advance because they are based on offline learning. This property is not suitable for online learning systems. To overcome this inconvenience, we propose a quick online variable selection method inspired by human problem solving behaviors. The proposed method tries to generate several variable set candidates in a speculative manner using a filter method and evaluates them using a wrapper method. The method can also function in concept-drifting environments, where relevant variable sets are changing. The experimental results show that the new method yields appropriate variable sets from a small number of samples.