memory based reasoning

7/28/2019 Memory Based Reasoning

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PREPARED BY:

ASHUTOSH KUMAR

(00316204411)

BHARTI SHARMA

(00716204411)

MEMORY BASED REASONING


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WHAT IS MBR?

MBR stands forMemory Based Reason ing.

In Memory Based Reasoning results are based

on analogous situations in the past.

Our ability to reason from experience dependson our ability to recognize appropriate

examples from the past.

These examples can be of: Movies, Food ,

Traff ic patterns and rou tes.

We identify similar example(s) and apply what

we know/learned to current situation.


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These similar examples in MBR are referred to as

Neighbors.

Identifying similar cases from experience.

Applying the information from these cases to the

problem at hand.

MBR finds neighbors similar to a new record anduses the neighbors for classification and

prediction.

By following all these basic information about

MBR, we came to know that basically it means

taking decisions for future depending on the past

experiences.


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MBR uses known instances of a model to predict

unknown instances.

This data mining technique maintains a dataset ofknown records.


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MEMORY BASED REASONING ALGORITHM:

This algorithm works on the principle in which firstwe identify similar instances in the past, then we

use the past instances and apply the information

about those instances to the present.

The algorithm knows the characteristics of the

records in this training dataset.

When a new record arrives for evaluation, the

algorithm finds similar to the new record then

uses the characteristics of the neighbor for

prediction and classification.


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When a new record arrives at the data mining tool,

first the tool calculates the distance between this

record and the records in the training dataset.

The results determine which data records in the

training dataset qualify to be considered as

neighbors to the incoming data record.

Next, the algorithm uses a combination function tocombine the results of the various distance functions

to obtain final answer.


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TWO BASIC OPERATIONS OF MBR ALGORITHM:

Distance function : assigns a distance between

any two records.

Combination function : combines the results fromthe neighbors to arrive at an answer


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APPLICATIONS OF MBR:

Fraud detection.

Customer response prediction.

Medical treatments.

Classifying responses MBR can process free-

text responses and assign codes.


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STRENGTHS OF MBR:

It produces results that are readilyunderstandable.

It is applicable to arbitrary data types,

even non-relational data. It works efficiently on almost any

number of fields.

Maintaining the training set requires aminimal amount of effort.


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The three main issues in solving a problem with MBR:

Selecting the most suitable historical recordsto form the training or base dataset.

Establishing the best way to compose the

historical record. Determining the two essential functions,

namely, the distance function and the

combination function.


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Weaknesses of Memory-Based Reasoning:

It is computationally expensive when doingclassification and prediction.

It requires a large amount of storage for the

training set.Results can be dependent on the choice of

distance function, combination function, and

number of neighbors.


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THANK YOU

memory based reasoning

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