An Evaluation of the Usefulness of Case-Based Explanation

Cunningham, Pádraig; Doyle, Dónal; Loughrey, John

doi:10.1007/3-540-45006-8_12

Cited by 90 publications

(71 citation statements)

References 6 publications

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“…A learning system that can provide good explanations for its predictions can increase user confidence and trust and give the user a sense of control over the system (Roth-Berghofer, 2004). Case-based explanations are generally based on a strategy of presenting similar past examples to support and justify the predictions made (Cunningham et al, 2003;Nugent et al, 2008). If specific cases are to be invoked as explanations then noisy cases need to be identified and removed from the case-base.…”

Section: Motivationmentioning

confidence: 99%

“…It is difficult to make instance-based learning algorithms such as k-nearest neighbour (k-NN) classifiers or case-based reasoning (CBR) noise tolerant so noise reduction can be important for improving generalisation accuracy in instance-based learning. A further motivation for noise reduction in CBR is explanation -a capability that is perceived to be one of the advantages of CBR (Leake, 1996;Cunningham et al, 2003). Since case-based explanation will invoke individual cases as part of the explanation process it is important that noisy cases can be eliminated if possible.…”

Section: Introductionmentioning

confidence: 99%

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Noise reduction for instance-based learning with a local maximal margin approach

Segata

Blanzieri

Delany³

et al. 2009

J Intell Inf Syst

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To some extent the problem of noise reduction in machine learning has been finessed by the development of learning techniques that are noise-tolerant. However, it is difficult to make instance-based learning noise tolerant and noise reduction still plays an important role in k-nearest neighbour classification. There are also other motivations for noise reduction, for instance the elimination of noise may result in simpler models or data cleansing may be an end in itself.In this paper we present a novel approach to noise reduction based on local Support Vector Machines (LSVM) which brings the benefits of maximal margin classifiers to bear on noise reduction. This provides a more robust alternative to the majority rule on which almost all the existing noise reduction techniques are based. Roughly speaking, for each training sample an SVM is trained on its neighbourhood and if the SVM classification for the central sample disagrees with its actual class there is evidence in favour of removing it from the training set. We provide an empirical evaluation on 15 real datasets showing improved classification accuracy when using training data edited with our method as well as specific experiments regarding the spam filtering application domain. We present a further evaluation on two artificial datasets where we analyse two different types of noise (Gaussian sample noise and mislabelling noise) and the influence of different class densities. The conclusion is that LSVM noise reduction is significatively better than the other analysed algorithms for real datasets and for artificial datasets perturbed by Gaussian noise and in presence of uneven class densities.

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Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise reduction for instance-based learning with a local maximal margin approach

Segata

Blanzieri

Delany³

et al. 2009

J Intell Inf Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…If they do not accept that the retrieved and current problems are similar, they are free to reject the proposed solution or treat it with some caution. Either way, presenting past cases to users gives a high degree of insight into the problem solving process and allows people to decide their own level of confidence in each solution [Cunningham et al 2003b]. This is particularly useful when the suggested solution is uncertain, perhaps because similar past cases contain contradictory solutions.…”

Section: Improved Performance During Operationmentioning

confidence: 99%

A Knowledge-Light Approach to Regression Using Case-Based Reasoning

McDonnell

Cunningham

2006

Lecture Notes in Computer Science

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“…But in addition users often need to be educated about the product space, especially if they are to come to understand what is available and why certain options are being recommended by the sales-assistant. Thus recommender systems also need to educate users about the product space: to justify their recommendations and explain the reasoning behind their suggestions; see, for example, [29,36,57,59,60,76,82,95] In summary then, case-based recommendation provides for a powerful and effective form of recommendation that is well suited to many product recommendation scenarios. As a style of recommendation, its use of case knowledge and product similarity, makes particular sense in the context of interactive recommendation scenarios where recommender system and user must collaborative in a flexible and transparent manner.…”

Section: Discussionmentioning

confidence: 99%

Case-Based Recommendation

Smyth

Lecture Notes in Computer Science

155

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Abstract.Recommender systems try to help users access complex information spaces. A good example is when they are used to help users to access online product catalogs, where recommender systems have proven to be especially useful for making product suggestions in response to evolving user needs and preferences. Case-based recommendation is a form of content-based recommendation that is well suited to many product recommendation domains where individual products are described in terms of a well defined set of features (e.g., price, colour, make, etc.). These representations allow case-based recommenders to make judgments about product similarities in order to improve the quality of their recommendations and as a result this type of approach has proven to be very successful in many e-commerce settings, especially when the needs and preferences of users are ill-defined, as they often are. In this chapter we will describe the basic approach to case-based recommendation, highlighting how it differs from other recommendation technologies, and introducing some recent advances that have led to more powerful and flexible recommender systems. IntroductionRecently I wanted to buy a new digital camera. I had a vague idea of what I wanted-a 6 mega-pixel digital SLR from a good manufacturer-but it proved difficult and time consuming to locate a product online that suited my needs, especially as these needs evolved during my investigations. Many online stores allowed me to browse or navigate through their product catalog by choosing from a series of static features (e.g., manufacturer, camera type, resolution, level of zoom etc.). Each time I selected a feature I was presented with the set of cameras with this feature and I could then go on to choose another feature to further refine the presented products. Other stores allowed me to search for my ideal camera by entering a query (e.g. "digital slr, 6 mega-pixels") and presented me with a list of results which I could then browse at my leisure.Both of these access options were helpful in different ways-in the beginning I preferred to browse through catalogs but, after getting a feel for the various features and compromises, I tended to use search-based interfaces-however neither provided P. Brusilovsky,

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An Evaluation of the Usefulness of Case-Based Explanation

Cited by 90 publications

References 6 publications

Noise reduction for instance-based learning with a local maximal margin approach

Noise reduction for instance-based learning with a local maximal margin approach

A Knowledge-Light Approach to Regression Using Case-Based Reasoning

Case-Based Recommendation

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