# A Fuzzy-Rough Approach for Case Base Maintenance

## Summary (1 min read)

### 1 Introduction

- At present, large-scale CBR systems are becoming more popular, with caselibrary sizes ranging from thousands [3][4] to millions of cases [5].
- Large case library sizes raise problems of case retrieval efficiency, and many CBR researchers pay more attention to the problem of Case Base Maintenance (CBM).
- Anand et al. [9] proposed to use data mining techniques for mining adaptation knowledge, and maintaining CBR systems.

### 2.1 Phase One - Learning Feature Weights

- The smaller the evaluation value, the better the corresponding features.
- Thus the authors would like to find the weights such that the evaluation function attains its minimum.
- When all the weights take value 1, the similarity measure is denoted by )1( pqSM .
- Select the parameter α and the learning rateη.

### 2.2 Phase Two - Partitioning the Case Library into Several Clusters

- This section attempts to partition the case library into several clusters by using the weighted distance metric with the weights learned in section 2.1.
- Since the considered features are considered to be real-valued, many methods, such as K-Means clustering [15] and Kohonen’s self-organizing network [16], can be used to partition the case library.
- In order to compare the fuzzy decision tree and fuzzyrough approaches in mining adaptation rules, the authors take the similarity matrix clustering method in [1].

### 2.4 Selecting Representative Cases

- This phase aims to select representative cases from each cluster according to the adaptation rules obtained in phase three.
- Instead of the deletion, [1] proposes a selection strategy which makes use of Smyth’s proposed concepts of coverage and reachability with some changes (called ε -coverage and ε -reachability respectively).

### 3 Experimental Analysis

- This section presents the experimental analysis of their approach on a real-world problem, i.e. the rice taste (RT) problem.
- There are 56 cases are deleted by using fuzzy rough approach while only 39 cases are deleted by using fuzzy decision tree method.
- So the overall selection result based on the adaptation rules generated by fuzzy-rough method is better than those based on the rules generated by the fuzzy decision tree.

Did you find this useful? Give us your feedback

##### Citations

56 citations

### Cites background or methods from "A Fuzzy-Rough Approach for Case Bas..."

...Therefore, to develop a CBR with appropriate feature selection and case organization as well as case base visualization is an urgent need to assist product design....

[...]

...Once feature selection is finished, the growing hierarchical self-organizing map (GHSOM) is taken as a cluster tool to organize those cases so that the initial case base can be divided into some small subsets with hierarchical structure....

[...]

...With the rapid development of CBR, large scale case base is becoming more common, with the number of instances ranging from thousands to millions (Cao et al., 2001)....

[...]

...Cao et al. (2001, 2003) proposed a fuzzy-rough method for case library maintenance and adaptation knowledge mining....

[...]

51 citations

### Cites background or methods from "A Fuzzy-Rough Approach for Case Bas..."

...Similar approaches have been proposed by Cabailero et al (2005) who creates the edited training data from the lower and upper set approximations and Cao et al (2001) who couples rough sets theory with fuzzy decision tree induction....

[...]

...Lorena and Carvalho (2004), for example, found that preprocessing the training data to remove noise resulted in simplifications in induced SVM classifiers and higher comprehensiveness in induced decision tree classifiers....

[...]

45 citations

30 citations

21 citations

### Cites background from "A Fuzzy-Rough Approach for Case Bas..."

...One branch of research has focused on the partitioning of case base which builds an elaborate CB structure and maintains it continuously [1], [5], [6]....

[...]

...On the other hand, following a partitioning policy [1], [6], [13] that builds an elaborate case base structure and maintains it continuously....

[...]

##### References

[...]

52,705 citations

15,662 citations

### "A Fuzzy-Rough Approach for Case Bas..." refers methods in this paper

...Since the considered features are considered to be real-valued, many methods, such as K-Means clustering [15] and Kohonen’s self-organizing network [16], can be used to partition the case library....

[...]

2,878 citations

935 citations

902 citations

### "A Fuzzy-Rough Approach for Case Bas..." refers background in this paper

...(Yuan and Shaw [17]) The true degree of fuzzy rule BA ⇒ is defined to be =α )(/))(),(min( uuuuuu Uu ABUu A ∑∑ ∈∈ , where A and B are two fuzzy sets defined on the same universe U. Definition 2....

[...]

...(Yuan and Shaw [17]) The true degree of fuzzy rule B A ⇒ is defined to be = α ) ( / )) ( ), ( min( u u u u u u U u A B U u A ∑ ∑ ∈ ∈ , where A and B are two fuzzy sets defined on the same universe U....

[...]

##### Related Papers (5)

##### Frequently Asked Questions (2)

###### Q2. What are the future works in "A fuzzy-rough approach for case base maintenance" ?

Future work includes ( 1 ) a large scale testing of their methodology using different case-bases, ( 2 ) the refining of the fuzzy-rough algorithms, ( 3 ) a comprehensive analysis of the complexity of the case base maintenance and reasoning algorithm in time and space, and ( 4 ) future comparison with other methods, such as fuzzy decision tree, C4. 5, genetic algorithm and so on.