1 Rough Sets Theory Speaker ： Kun Hsiang. 2 Outline Introduction Basic concepts of the rough sets theory Decision table Main steps of decision table analysis

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Rough Sets Theory

Speaker： Kun Hsiang

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Outline

• Introduction

• Basic concepts of the rough sets theory

• Decision table

• Main steps of decision table analysis

• An illustrative example of application of the rough set approach

• Discussion

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Introduction

• Often, information on the surrounding world is– Imprecise– Incomplete– uncertain.

• We should be able to process uncertain and/or incomplete information.

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Introduction

• When dealing with inexact, uncertain, or vague knowledge, are the fuzzy set and the rough set theories.

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Introduction

• Fuzzy set theory– Introduced by Zadeh in 1965 [1]– Has demonstrated its usefulness in chemistry

and in other disciplines [2-9]

• Rough set theory– Introduced by Pawlak in 1985 [10,11]– Popular in many other disciplines [12]

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Introduction

• Fuzzy Sets

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Introduction

• Rough Sets– In the rough set theory, membership is not

the primary concept.– Rough sets represent a different mathematical

approach to vagueness and uncertainty.

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Introduction• Rough Sets

– The rough set methodology is based on the premise that lowering the degree of precision in the data makes the data pattern more visible.

– Consider a simple example. Two acids with pKs of

respectively pK 4.12 and 4.53 will, in many contexts,

be perceived as so equally weak, that they are

indiscernible with respect to this attribute.– They are part of a rough set ‘weak acids’ as compa

red to ‘strong’ or ‘medium’ or whatever other category, relevant to the context of this classification.

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Basic concepts of the rough sets theory

• Information system– IS=(U,A)

– U is the universe ( a finite set of objects, U={x1,x2,….., xm}

– A is the set of attributes (features, variables)

– Va is the set of values a, called the domain of attribute a.

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Consider a data set containing the results of threemeasurements performed for 10 objects. The resultscan be organized in a matrix10x3.


Measurements

Objects

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• IS=(U,A)

• U={x1,x2,x3,x4,x5,x6….., x10}

• A={a1,a2,a3}

• The domains of attributes are：V1={1,2,3}

V2={1,2}

V3={1,2,3,4}

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If X is crisp with respect to B.

If X is rough with respect to B.

,1)( XB,1)( XB

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• If the set of attributes is dependent, one can be interested in finding all possible minimal subsets of attributes.

• The concepts of core and reduct are two fundamental concepts of the rough sets theory.– Keep only those attributes that preserve the indiscerni

bility relation and, consequently, set approximation. – There are usually several such subsets of attributes a

nd those which are minimal are called reducts.

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• To compute reducts and core, the discernibility matrix is used.

• The discernibility matrix has the dimension nxn.

• where n denotes the number of elementary sets and its elements are defined as the set of all attributes which discern elementary sets [x]i and [x]j .

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Discerns set 1 from both set 2 and set 3

Discerns set 1 from sets 2,3,4 and set 5

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The discernibility function has the following form:

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Classification：

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Basic concepts of the rough sets theoryDecision Table：

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An illustrative example of application of therough set approach

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Discussion

Documents

1 Rough Sets Theory Speaker ： Kun Hsiang. 2 Outline Introduction Basic concepts of the rough sets theory Decision table Main steps of decision table analysis