Metagenome shotgun sequencing presents opportunities to identify organisms that may prevent or promote disease. Analysis of sample diversity is achieved by taxonomic identification of metagenomic reads followed by generating an abundance profile. Numerous tools have been developed for taxonomic identification based on different design principles. Tools that have been designed to achieve high precision and practical performance still lack sensitivity. Moreover, tools with the highest sensitivity suffer from low precision, low specificity along with long computation time. In this paper, we present WEVOTE (WEighted VOting Taxonomic dEntification), a method that classifies metagenome shotgun sequencing DNA reads based on an ensemble of existing methods using k-mer based, marker-based, and naive-similarity based approaches. Our evaluation, based on fourteen benchmarking datasets, shows that WEVOTE reduces occurrence of the false positives to half of that produced by other high sensitive tools while also maintaining the same level of sensitivity. WEVOTE is an efficient, automated tool that combines multiple individual taxonomic identification methods. It is expandable and has the potential to reduce false positives and produce a more accurate taxonomic identification for microbiome data. WEVOTE was implemented using C++ and shell script and is available at https://bitbucket.org/ametwally/wevote.