# ML | Dampster Shafer Theory

**Dempster Shafer Theory** is given by Arthure P.Dempster in 1967 and his student Glenn Shafer in 1976.

This theory is being released because of following reason:-

- Bayesian theory is only concerned about single evidences.
- Bayesian probability cannot describe ignorance.

DST is an evidence theory, it combines all possible outcomes of the problem. Hence it is used to solve problems where there may be a chance that a different evidence will lead to some different result.

The **uncertainity in this model** is given by:-

- Consider all possible outcomes.
- Belief will lead to believe in some possiblity by bringing out some evidence.
- Plausibility will make evidence compatiblity with possible outcomes.

**For eg:- **

let us consider a room where four person are presented A, B, C, D(lets say) And suddenly lights out and when the lights come back B has been died due to stabbing in his back with the help of a knife. No one came into the room and no one has leaved the room and B has not committed suicide. Then we have to find out who is the murdrer?

To solve these there are the **following possibilities**:

- Either {A} or{C} or {D} has killed him.
- Either {A, C} or {C, D} or {A, C} have killed him.
- Or the three of them kill him i.e; {A, C, D}
- None of the kill him {o}(let us say).

These will be the possible evidences by which we can find the murderer by measure of plausiblity.

Using the above example we can say :

Set of possible conclusion (P): {p1, p2….pn}

where P is set of possible conclusion and cannot be exhaustive means at least one (p)i must be true.

(p)i must be mutually exclusive.

Power Set will contain 2^{n} elements where n is number of elements in the possible set.

For eg:-

If P = { a, b, c}, then Power set is given as

{o, {a}, {b}, {c}, {a, b}, {b, c}, {a, c}, {a, b, c}}= 2^{3} elements.

**Mass function m(K):** It is an interpretation of m({K or B}) i.e; it means there is evidence for {K or B} which cannot be divided among more specific beliefs for K and B.

**Belief in K:** The belief in element K of Power Set is the sum of masses of element which are subsets of K. This can be explained through an example

Lets say K = {a, b, c}

Bel(K) = m(a) + m(b) + m(c) + m(a, b) + m(a, c) + m(b, c) + m(a, b, c)

**Plaausiblity in K:** It is the sum of masses of set that intersects with K.

i.e; Pl(K) = m(a) + m(b) + m(c) + m(a, b) + m(b, c) + m(a, c) + m(a, b, c)

**Characteristics of Dempster Shafer Theory:**

- It will ignorance part such that probability of all events aggregate to 1.
- Ignorance is reduced in this theory by adding more and more evidences.
- Combination rule is used to combine various types of possiblities.

**Advantages:**

- As we add more information, uncertainty interval reduces.
- DST has much lower level of ignorance.
- Diagnose Hierarchies can be represented using this.
- Person dealing with such problems is free to think about evidences.

**Disadvantages:**

- In this computation effort is high, as we have to deal with 2
^{n}of sets.

## Recommended Posts:

- Rough Set Theory | An Introduction
- Rough Set Theory | Properties and Important Terms | Set - 2
- Bias Vs Variance in Machine Learning
- How Machine Learning and Artificial Intelligence Will Impact Global Industries in 2020?
- What is Business Intelligence?
- ML | Linear Regression vs Logistic Regression
- Keras vs PyTorch
- Introduction to Data Science
- ML | Bias-Variance Trade off
- ML | Naive Bayes Scratch Implementation using Python
- Precision and Recall in Information Retrieval
- Effect of Google Quantum Supremacy on Data Science
- Capsule Neural Networks | ML
- How To Use Jupyter Notebook - An Ultimate Guide

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.