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For pre-processing, three filtering techniques have been used. However, these traditional filtering techniques face difficulties for large datasets due to the long required time during large data processing as well as the large required memory size. In this regard, a mapping-based neural skyline filtering approach is designed. Randomized algorithm performed the mapping for large volume of datasets based on objective function. The objective function determines the randomized size of the datasets according to the homogeneity. Around 200 million DNA base pairs have been used for experimental analysis. Experimental result shows that mapping centric filtering outperforms other filtering techniques during large data processing.<\/jats:p>","DOI":"10.1142\/s1469026817500195","type":"journal-article","created":{"date-parts":[[2017,9,8]],"date-time":"2017-09-08T10:13:22Z","timestamp":1504865602000},"page":"1750019","source":"Crossref","is-referenced-by-count":8,"title":["Neural Skyline Filtering for Imbalance Features Classification"],"prefix":"10.1142","volume":"16","author":[{"given":"Sonia Farhana","family":"Nimmy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Notre Dame University Bangladesh, Bangladesh"}]},{"given":"Md. 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