Large – Scale Multimodality Attribute Reduction with Multi – Kernel Fuzzy Rough Sets

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Multimodality Attribute Reduction With Multi-Kernel Fuzzy Rough Sets

Large – Scale Multimodality Attribute Reduction with Multi – Kernel Fuzzy Rough Sets

Abstract

Large – Scale Multimodality Attribute Reduction with Multi – Kernel Fuzzy Rough Sets java project report In complex pattern recognition tasks, objects are typically characterized by means of multimodality attributes, including categorical, numerical, text, image, audio and even videos. In these cases, data are usually high dimensional, structurally complex, and granular. Those attributes exhibit some redundancy and irrelevant information. The evaluation, selection, and combination of multi-modality attributes pose great challenges to traditional classification algorithms.

Multikernel learning handles multi-modality attributes java project report by using di_erent kernels to extract information coming from di_erent attributes. However, it cannot consider the aspects fuzziness in fuzzy classification. Fuzzy rough sets emerge as a powerful vehicle to handle fuzzy and uncertain attribute reduction. In this paper, we design a framework of multi-modality attribute reduction based on multi-kernel fuzzy rough sets. First, a combination of kernels based on set theory is defined to extract fuzzy similarity for fuzzy classification with multi-modality attributes. Then, a model of multi-kernel fuzzy rough sets is constructed.

System Configuration:

H/W System Configuration:-

System             : I3 Processor.

Hard Disk          : 500 GB.

Monitor             : 15’’ LED

Input Devices    : Keyboard, Mouse

Ram                 : 4 GB

S/W System Configuration:-

Operating system    : Windows 7/UBUNTU.

Coding Language     : Java 1.7 ,Hadoop 0.8.1

IDE                        : Eclipse

Database                : MYSQL

Conclusions

In Large-Scale Multimodality Attribute Reduction With Multi-Kernel Fuzzy Rough Sets java project report study, the model of multi-kernel fuzzy rough sets was developed by integrating multi-kernel learning with fuzzy rough sets. We have designed an algorithm of large-scale multi-modality attribute reduction based on this model. First, we defined a novel combination of kernels based on T-norm to determine the fuzzy similarity between multi-modality data. Then, we have proposed the model of multi-kernel fuzzy rough sets.

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Name of the Project   : Large – Scale Multimodality Attribute Reduction with Multi – Kernel Fuzzy Rough Sets

Project Cost                : $ 50

Delivery Time             :  Within 48 hours

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