Rough Sets:

We first introduce the concept of the lower approximation in rough set theory. Subsequently, we present the notion of the upper approximation. Thereafter, to improve understanding, we provide a representative rough set example. Through this illustrative example, we clearly demonstrate the process of data granulation and further explain how data reduction is achieved.

What is a Rough Set?

A Rough Set is used to represent a set of objects that cannot be precisely defined using the available information. It approximates a “target set” by using two precise sets: the Lower Approximation and the Upper Approximation.

Key Core Concepts:Universe (U): The collection of all objects.
Indiscernibility Relation: When two objects are identical based on their properties, they are indiscernible.
Elementary Sets: Groups of objects that look exactly the same.

The Two Approximations

  • Lower Approximation (RX): The set of objects that certainly belong to the target set.
  • Upper Approximation (R̄X): The set of objects that possibly belong to the target set.
  • Boundary Region: The difference between the two. If the boundary is empty, the set is crisp; otherwise, it is “Rough.”

Clear Example: Patient Diagnosis

Suppose we have 5 patients {p1, p2, p3, p4, p5}. Based on their symptoms, we group them into elementary sets (objects that look the same):

Elementary Set 1: {p1, p2} (These two have identical symptoms)
Elementary Set 2: {p3} (Unique symptoms)
Elementary Set 3: {p4, p5} (Identical symptoms)

Now, let the target set X be patients who actually have “Flu”: X = {p1, p2, p3, p4}.

Step-by-Step Approximation: 1. Lower Approximation (RX): Which groups are fully inside X?
    {p1, p2} is in X. {p3} is in X.
    RX = {p1, p2, p3}

2. Upper Approximation (R̄X): Which groups have at least one member in X?
    {p1, p2} has members in X. {p3} has members in X. {p4, p5} has p4 in X.
    R̄X = {p1, p2, p3, p4, p5}

3. Boundary Region: R̄X minus RX = {p4, p5}. (Uncertain region).

Operations of Rough Sets

Operations on Rough Sets are performed by calculating the approximations of the resulting set.

OperationLower Approximation RuleUpper Approximation Rule
Union (A ∪ B)R(A) ∪ R(B)R̄(A ∪ B)
Intersection (A ∩ B)R(A ∩ B)R̄(A) ∩ R̄(B)
Example (Intersection): If Expert A says the set is ({p1}, {p1, p2}) and Expert B says ({p1, p3}, {p1, p2, p3}):
The Intersection Lower Approximation is the common “certain” elements: {p1}.

Rough Numbers (RN)

A Rough Number is defined by its lower limit and upper limit, derived from the approximations. It is written as: RN = [L, U]

Operations of Rough Numbers

Let RN1 = [L1, U1] and RN2 = [L2, U2].

1. Addition (+): Formula: [L1 + L2, U1 + U2]
Example: [2, 5] + [3, 4] = [5, 9]
2. Subtraction (-): Formula: [L1 – U2, U1 – L2]
Example: [10, 15] – [2, 5] = [10-5, 15-2] = [5, 13]
3. Multiplication (×): Formula: [L1 × L2, U1 × U2]
Example: [2, 3] × [4, 6] = [8, 18]
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Machine Learning Fuzzy Sets Computational Math Graph Theory
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