Privacy preserving sub-feature selection based on fuzzy probabilities

The feature selection addresses the issue of developing accurate models for classification in data mining. The aggregated data collection from distributed environment for feature selection makes the problem of accessing the relevant inputs of individual data records. Preserving the privacy of individual data is often critical issue in distributed data mining. In this paper, it proposes the privacy preservation of individual data for both feature and sub-feature selection based on data mining techniques and fuzzy probabilities. For privacy purpose, each party maintains their privacy as the instruction of data miner with the help of fuzzy probabilities as alias values. The techniques have developed for own database of data miner in distributed network with fuzzy system and also evaluation of sub-feature value included for the processing of data mining task. The feature selection has been explained by existing data mining techniques i.e., gain ratio using fuzzy optimization. The estimation of gain ratio based on the relevant inputs for the feature selection has been evaluated within the expected upper and lower bound of fuzzy data set. It mainly focuses on sub-feature selection with privacy algorithm using fuzzy random variables among different parties in distributed environment. The sub-feature selection is uniquely identified for better class prediction. The algorithm provides the idea of selecting sub-feature using fuzzy probabilities with fuzzy frequency data from data miner's database. The experimental result shows performance of our findings based on real world data set.