Inductive Learning - decision trees

Decision Trees

Quinlan in his ID3 system (986) introduced the idea of decision trees.
ID3 is a program that can build trees automatically from given positive and negative instances.
Basically each leaf of a decision tree asserts a positive or negative concept. To classify a particular input we start at the top and follow assertions down until we reach an answer (Fig 28)
  
Fig. 28 Edible Mushroom decision tree
Building decision trees

• ID3 uses an iterative method.
• Simple trees preferred as more accurate classification is afforded.
• A random choice of samples from training set chosen for initial assembly of tree -- the window subset.
• Other training examples used to test tree.
• If all examples classified correctly stop.
• Otherwise add a number of training examples to window and start again.

Adding new nodes
When assembling the tree we need to choose when to add a new node:

• Some attributes will yield more information than others.
• Adding a new node might be useless in the overall classification process.
• Sometimes attributes will separate training instances into subsets whose members share a common label. Here branching can be terminates and a leaf node assigned for the whole subset.

Decision tree advantages:

• Quicker than version spaces when concept space is large.
• Disjunction easier.

Disadvantages:

• Representation not natural to humans -- a decision tree may find it hard to explain its classification.