Applying ensemble feature selection (EFS) models in various problems has not been actively discussed, and there has been a lack of effort to make it applicable in the situations such as distributed environments. Due to restrictions of centralized algorithms such as their poor scalability in the high dimension data and also distributed nature of some data, using the traditional centralized computing for dealing with such problems may be inevitable. This paper aims to develop a homogenous distributed ensemble feature selection (HMDE-FS) framework through a distributed resampling approach rather than a centralized one. The homogenous ensembles mainly operate along with a resampling process, so applying various methods to resampling can affect the performance of the model. Among various strategies, those with and without replacement are two of the main technique families, hence we investigated the efficiency of two well-known with/without replacement techniques: bootstrapping (BS) and cross-validation (CV) inspired method that we named crisscross (CC). The proposed HMDE-FS approaches are tested on eight datasets, and the heavy experimental results illustrate that these methods considerably reduce runtime, while classification accuracy maintains its competitiveness.
