Distinguish wild mushrooms with decision tree
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Distinguish Wild Mushrooms with Decision Tree. Shiqin Yan. Objective. Utilize the already existed database of the mushrooms to build a decision tree to assist the process of determine the whether the mushroom is poisonous . DataSet.

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Distinguish Wild Mushrooms with Decision Tree

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Distinguish wild mushrooms with decision tree

Distinguish Wild Mushrooms with Decision Tree

Shiqin Yan


Objective

Objective

  • Utilize the already existed database of the mushrooms to build a decision tree to assist the process of determine the whether the mushroom is poisonous.


Dataset

DataSet

  • Existing record drawn from the Audubon Society Field Guide to North American Mushrooms (1981) . G. H. Lincoff (Pres. ), NewYork: Alfred A. Knopf

  • Number of Instances: 8124 (classified as either edible or poisonous)

  • Number of Attributes: 22

  • Training: 5416, Tuning: 1354, Testing: 1354

  • Missing attribute values: 2480 (denoted by “?”), all for attribute 11


Mushroom features

Mushroom Features

  • 1. cap-shape: bell=b, conical=c, convex=x, flat=f, knobbed=k, sunken = s

  • 2. cap-surface: fibrous=f, grooves=g, scaly=y, smooth=s

  • 3. cap-color: brown=n, buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y

  • 4. bruise?: bruises=t, no=f

  • 5. odor: almond=a, anise=l, creosote=c, fishy=y, foul=f


Approach

Approach

  • Mutual information to determine the features used to split the tree.

  • Mutual information:

  • Y: label, X: feature

  • Choose feature X which maximizes I(Y;X)


Most informative features extracted from decision tree

Most informative features extracted from decision tree:

  • odor

  • spore-print-color

  • habitat

  • population


Prior research

Prior Research

by WlodzislawDuch, Department of Computer Methods, Nicholas Copernicus University


Future

Future

  • Add cross-validation to improve the accuracy

  • Prune the tree to avoid over-fitting


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