Development of Data Mining Models Based on Features Ranks Voting (FRV)

Data size plays a significant role in the design and performance of data mining models. A good feature selection algorithm reduces problems big noise due to redundancy. Features algorithms aim at selecting best features eliminating unnecessary ones, which turn simplifies structure model as well increases its performance. This paper introduces robust algorithm, named Ranking Voting Algorithm FRV. It merges benefits different specify ranks dataset correctly robustly; based on voting algorithm. The FRV comprises three proposed techniques select minimum set, forward technique high features, backward technique, drops low (low importance feature), third outputs from maximize robustness selected set. Different models were built using obtained sets applying FVR datasets; evaluate success behavior these reflects is compared with other algorithms. successes develop for Hungarian CAD Acc. 96.8%, 96% Z-Alizadeh Sani 83.94% 92.56% respectively [48].