Modelling the competence of case-bases

Smyth, Barry; McKenna, Elizabeth

doi:10.1007/bfb0056334

Cited by 104 publications

(64 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The noise reduction technique we present, which we call Blame-Based Noise Reduction (BBNR), extends the competence based modelling ideas of Smyth and colleagues [4,5]. Their case coverage measure, used in case selection, indicates how well a case contributes to correctly classifying other cases in the case-base.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Case-Base Editing in a Spam Filtering System

Delany¹,

Cunningham

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.Because of the volume of spam email and its evolving nature, any deployed Machine Learning-based spam filtering system will need to have procedures for case-base maintenance. Key to this will be procedures to edit the case-base to remove noise and eliminate redundancy. In this paper we present a two stage process to do this. We present a new noise reduction algorithm called Blame-Based Noise Reduction that removes cases that are observed to cause misclassification. We also present an algorithm called Conservative Redundancy Reduction that is much less aggressive than the state-of-the-art alternatives and has significantly better generalisation performance in this domain. These new techniques are evaluated against the alternatives in the literature on four datasets of 1000 emails each (50% spam and 50% non spam).

show abstract

Section: Introductionmentioning

confidence: 99%

An Analysis of Case-Base Editing in a Spam Filtering System

Delany¹,

Cunningham

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…F 2 . In [2], we also describe 4 additional measures: one is from the Case-Based Reasoning (CBR) literature [7]; we define the other 3 from ideas presented in the CBR literature [12,16]. Table 2 [9,10], and we have also shown these in the Table. For the purposes of this paper, we select just three of the complexity measures, one from each of the categories.…”

Section: Measures For Maintenancementioning

confidence: 99%

On Dataset Complexity for Case Base Maintenance

Cummins

Bridge

2011

Case-Based Reasoning Research and Development

View full text Add to dashboard Cite

Abstract.We present what is, to the best of our knowledge, the first analysis that uses dataset complexity measures to evaluate case base editing algorithms. We select three different complexity measures and use them to evaluate eight case base editing algorithms. While we might expect the complexity of a case base to decrease, or stay the same, and the classification accuracy to increase, or stay the same, after maintenance, we find many counter-examples. In particular, we find that the RENN noise reduction algorithm may be over-simplifying class boundaries.

show abstract

“…Measures of geometry, topology and density of manifolds: These measures characterize the position, shape, overlap and density of the manifolds that contain the instances of each class. [24]. Table 1 summarizes these seventeen measures.…”

Section: Meta-problem Descriptionmentioning

confidence: 99%

Choosing a Case Base Maintenance Algorithm using a Meta-Case Base

Cummins

Bridge

2011

Research and Development in Intelligent Systems XXVIII

View full text Add to dashboard Cite

In Case-Based Reasoning (CBR), case base maintenance algorithms remove noisy or redundant cases from case bases. The best maintenance algorithm to use on a particular case base at a particular stage in a CBR system's lifetime will vary. In this paper, we propose a meta-case-based classifier for selecting the best maintenance algorithm. The classifier takes in a description of a case base that is to undergo maintenance, and uses meta-cases-descriptions of case bases that have undergone maintenance-to predict the best maintenance algorithm. For describing case bases, we use measures of dataset complexity. We present the results of experiments that show the classifier can come close to selecting the best possible maintenance algorithms.

show abstract

Modelling the competence of case-bases

Cited by 104 publications

References 6 publications

An Analysis of Case-Base Editing in a Spam Filtering System

An Analysis of Case-Base Editing in a Spam Filtering System

On Dataset Complexity for Case Base Maintenance

Choosing a Case Base Maintenance Algorithm using a Meta-Case Base

Contact Info

Product

Resources

About