Large-Scale Multimodality Attribute Reduction With Multi-Kernel Fuzzy Rough Sets

Large-Scale Multimodality Attribute Reduction With Multi-Kernel Fuzzy Rough Sets is a report that focuses on the necessity of attribute reduction with multi-kernel fuzzy. The multimodality attributes include text, numbers, image, audio, video, etc. Irrelevant information is executable through the use of such attributes. For the traditional classification algorithms, the multi-modularity attributes pose a great challenge. It can easily be handled using the multi-kernel fuzzy rough sets. To handle the fuzzy logic, the fuzzy rough sets play a major role. The novel combination of the data can easily help in solving the work related to the multi-kernel fuzzy easily. The abstract on mini project report on large-scale multimodality attribute reduction with multi-kernel fuzzy rough sets is available. The users can free download abstract, synopsis on pdf to understand the effects of large-scale multimodality attribute reduction with multi-kernel fuzzy rough sets.

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Topics Covered:
01)Introduction
02)Objectives, ER Diagram
03)Flow Chats, Algorithms used
04)System Requirements
05)Project Screenshots
06)Conclusion, References