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layout: post title: "Machine Learning笔记 第09周" date: "2016-03-17 13:31:54" categories: 计算机科学 excerpt: "The aim of Feature selection can be find the knowledge in data and reduc..."

auth: conge

• content {:toc}

• The aim of Feature selection can be find the knowledge in data and reduce dimentionality of data.
  • with less features, it's easier to interpret data and get insight with data
  • the amount of data needed for solving ML problems grows exponentially as the number of features grows. So it's better to reduce the number of features.

• It is NP-hard and it is exponential.

Filtering and Wrapping

• Filtering is forward flow, there is no feedback from learning to the searching algorithm
• Wrapping has the searching algorithm inside with the learning algorithm and allows feedback from learning to the search algorithm.

• Filtering
  • Pros: fast
  • Cons: 1. slow for isolated features; 2 ignores the learning problem
• Wrapping
  • Pros: 1. takes into account of model bias; 2. takes into account of learning
  • cons: very slow.
• example of filtering: use DT to select important features for the learning algorithms (e.g. kNN).

For filtering Criteria:

• Information gain
• variation, entropy
• independent/non-redundant

How to do Wrapping:

• hill climbing
• randomized optimization
• Forward search: find the best feature first. then in the rest feature, find one and combine with the first selected feature which give the best the score and keep it; then find the one which get the best score when combined with the selected……
• backward search: remove one, for the rest of combinations, keep the one does the best, repeat... until the score change too much?

• For DT, it's easy. when a == 0, then label is -; when a == 1, then split on b, and when b == 0, label is -; when b == 1, label is +. This is a AND B.
• For the perceptron (w^Tx > 0), it is not that easy to see the results. With a and b, the problem is not solvable. adding c with weight of -1, the problem can be solved. Although c does not offer any information, it is still useful in this case.

• B.O.C:Bayes optimal classifier. Relevance only concerns B.O.C.
• Strongly relevant: removing x degrades B.O.C, then x is strongly relevent
• weakly relevant: when x is not strongly relevent and exits subset of features that addig x to it improves B.O.C
• irrelevant: NOT( strongly or weakly relevant)

2016-03-16