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🍄 Extract logical rules for mushroom edibility: Neural Networks; Genetic Algorithm + Decision Tree

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Two Approaches to Extract Logical Rules for Mushroom Edibility: Neural Networks and Genetic Algorithm

Project for ANU COMP4660/8420 (Bio-inspired Computing: Applications and Interfaces), Semester 1 2018.

By Yuanbo Han, 2018-05-31. See the project report here.

Environment

  • Python 3.6.3
    • numpy 1.14.3
    • matplotlib 2.2.2
    • pandas 0.22.0
    • torch 0.4.0
    • sklearn 0.19.1
    • pydotplus 2.0.2
  • graphviz 2.40.1

Note that the above are just versions during experiment, not the least requirements.

Data Set

Mushroom Data Set/agaricus-lepiota.data.csv

Original source: UCI Machine Learning Repository

Codes

  • bpNN.py
  • decisionTree.py
  • displayWeight.py
  • GATree.py
  • load_data.py

BP Neural Network

Neural Network for Extracting Rules

Run bpNN.py. It will read in the data, perform discretization, train a back-propagation neural network, and generate a file called "net_weights" which stores the weights in the model. To adjust parameters, see line 14~26. To change the network structure, see line 29~35.

Run displayWeight.py. It will read "net_weights" file and print the network weights for attribute values.

GA + Decision Tree

Decision Tree Selected by GA

Run GATree.py. It will read in the data, perform Genetic Algorithm for feature selection, and generate a "tree.pdf" which is the diagram of the final Decision Tree. Control parameters can be adjusted in line 6~13.

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🍄 Extract logical rules for mushroom edibility: Neural Networks; Genetic Algorithm + Decision Tree

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