本次作业涉及算法代写和Python代写
1 Educational Goal
Practice how to leverage online learning algorithms for feature selection.
2 Details
Project goal: Implement the Scalable and Accurate OnLine Approach (SAOLA) to learn how to apply
online learning to feature selection.
Due Date: 4:00 pm, November 28, 2017
Programming language: Python 2.7.
3 Tasks
3.1 Task 1
Review the lecture notes in “CS670_Team_Project_Part1.pdf” and implement the Scalable and Accurate
OnLine Approach to feature selection (SAOLA). Apply it to the experimental samples which you created
in Phase 1 to get the most relevant features (Markov Blanket).
3.2 Task 2 (Optional) Bonus points
Modify SAOLA to improve its performance on unbalanced data.
3.3 Task 3 (Optional) Bonus points
Highlight the relevant features you got on a map.
A example solution of task 3
Term Project Phase 1 Page 1 / 2
4 Submission Requirements
Submit the code and a report (include the important features you identified in task 1. If you finish task
2 and task 3, please submit a summary of your improvement for task 2 and your map image for task 3)
through your UMassOnline account. Each team can submit a single solution through the team lead’s
UMassOnline account; the submission should include a list of all team members’ names.