Project 2 k-NN 2018/11/10.

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Presentation transcript:

Project 2 k-NN 2018/11/10

Data Data: wine.data (can be opened with Text Editors, e.g. Notepad, Word, …) These data came from the chemical analysis results of different categories of wine For each sample, 13 attributes are provided Please read the readme.txt carefully Source: http://archive.ics.uci.edu/ml/datasets/Wine Problem: classify the wine into one of three categories 2018/11/10

Requirement Basic (7/10) Plus (at most 3/10) 1) Develop a k-NN classifier with Euclidean distance and simple voting 2) Perform 5-fold cross validation, find out which k performs the best (in terms of accuracy) 3) Use PCA to reduce the dimensionality, then perform 2) again. Does PCA improve the accuracy? Which dimensionality is better? Plus (at most 3/10) Try other distance metrics or distance-based voting (+0.5~1) Try other dimensionality reduction methods (+1~2) Perform feature selection (+1) How to set the k value, if not using cross validation? Verify your idea (+3) 2018/11/10

Report .pdf is best, .doc and .ppt are acceptable English is encouraged List all references Source code is a plus 2018/11/10