Adaptive decision making system you need to create adaptive decision making syst

Question

Adaptive decision making system

you need to create adaptive decision making system. You can choose any programming language but system should have simple graphical user interface.

Decision tree can be static or can be generated dynamically any time starting programme –you should explain tree generation technique during programme presentation.

System should have basic predefined priority tag values for any decision in your tree.

User can select tree solutions and system should provide user with best step chain how to get this solution.

TASK REQUIREMENTS

1. Define at least 4 level decision tree with priority tags. (you can use any decision tree topic – any application domain can be observed)

2. Manually recalculate tags value based on its priority – identify repeated object, and recalculate priority tag value of default decision tree.

3. Create application to identify best solution for selected problem:
a. User can adapt decision by selecting any 3 result value from existing tree - Order of selection should be identify in solution chain making – first selected option will have higher tag value. Selection should recalculate total solution tag value for priority.
b. System should provide solution chain how to get selected results – system should select best chain order according to recalculated object and decision tag values.

This sample decision tree has been made manually from google search by searching '' Computer d '' and see what results and priority tags has.

0.9 Desk 0.9 0.7 0.8 With hutch Top kea 0.9 0.7 0.8 Definition Wallpaper mages Computer 0.7 0.8 Definition Desktop 0.7 0.9 0.9 T 0.7 0.8 0.8 Wikipedia pdf In hindi Definition Wallpaper Images 0.9 0.7 0.7 0.9 0.8- Pdf Defined Mouse Digita Hardware Wikipedia

Explanation / Answer

Decision tree builds classification or regression models in the form of a tree structure.
It breaks down a dataset into smaller and smaller subsets while at the same time an associated
decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes.

An example of 4 level Decision Tree could be -

A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and Rainy).
Each of Sunny, Overcast, Rainy will have True and False as there branches.
At the 4th level, we have Play and NotPlay as the leaf nodes.

Leaf node (e.g., Play) represents a classification or decision.

The topmost decision node in a tree which corresponds to the best predictor called root node.

Decision trees can handle both categorical and numerical data.

The core algorithm for building the decision trees called ID3 employs Top Down greedy approach.
ID3 internally uses Entropy to construct decision tree.

ID3 uses homogenity to partition data into subsets with similar values.
If the sample is completely homogeneous the entrpy is 0 and if the sample is equally divided, the
entropy is 1.

Now to recalculate the tags based on priority, we need to recalculate the Entropy at every decision.

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