Hybrid Model of whale Optimization Merged Decision Tree For Classification
Feature Selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. The problem of choosing a feature is to find the most important subset of the main attributes in a specific domain, and its main purpose is removing additional or unrelated feat
Project Title
Hybrid Model of whale Optimization Merged Decision Tree For Classification
Project Area of Specialization
Project Summary
Feature Selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. The problem of choosing a feature is to find the most important subset of the main attributes in a specific domain, and its main purpose is removing additional or unrelated features, and ultimately improving the accuracy of the classification algorithms. As a result, the problem of FS can be considered as an optimization problem, and use metaheuristic algorithms to solve it..
Project Objectives
We propose a novel method for feature selection utilizing Whale optimization combined with a decision tree as the classifier to select a small number of informative gene from the thousands of gene in the data that can contribute to identifying cancers.
Project Implementation Method
We uses Pyhton language in Spyder IDE for implementation of our project and a dedicated server will be use.
Benefits of the Project
we wants to implement our project in different industries but specially it will be design for hospital and our aim is diagnose of brest cancer disease.
Technical Details of Final Deliverable
It will Help the doctors to identify the brest cancer of a patient and also classify where the cancer is benign or melignent.
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Hardware | Equipment | 1 | 60000 | 60000 |
| Total in (Rs) | 60000 |
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