Analogy-based classifiers for nominal or numerical data

Bounhas, Myriam; Prade, Henri; Richard, Gilles

doi:10.1016/j.ijar.2017.08.010

Cited by 37 publications

(27 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…">

O d d_{2} (N N, S t d)

seems to be also efficient when classifying data sets with a large number of attributes as in the case of “Car” and “Ionosphere” and large number of instances as in the case of “Magic,” “Sat.Image,” and “Segment” for instance.

O d d_{2} (N N, S t d)

has close classification results to those of analogy‐based classifier in the numerical case for most datasets. In the Boolean case, both

o d d n e s s

‐based and

a n a l o g y

‐based classifiers achieve good results for “Balance,” “Car,” “Monk1,” and “Monk3.” For “Monk2” data set, Analogy‐based classifier significantly outperforms

O d d_{2} (N N, S t d)

while for “Spect” and “Voting” the converse is observed.…”

Section: Experimentations and Preliminary Discussionmentioning

confidence: 68%

“…

O d d_{2} (N N, S t d)

has close classification results to those of analogy‐based classifier in the numerical case for most datasets. In the Boolean case, both

o d d n e s s

‐based and

a n a l o g y

‐based classifiers achieve good results for “Balance,” “Car,” “Monk1,” and “Monk3.” For “Monk2” data set, Analogy‐based classifier significantly outperforms

O d d_{2} (N N, S t d)

while for “Spect” and “Voting” the converse is observed. However, even if there is a path (through logical proportions, in the Boolean case) relating the respective building blocks on which analogy‐based classifiers and the classifiers studied here are based, the two types of classifiers seem to rely on different ideas: the control of the dissimilarity via the oddness measure, and the fact of privileging linearity in the other case …”

Section: Experimentations and Preliminary Discussionmentioning

confidence: 81%

“…The method had a cubic complexity due to the search for suitable triples of examples for building analogical proportions with the item to be classified. A computational improvement of the previous analogical classifier has been recently made …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Oddness-based classification: A new way of exploiting neighbors

Bounhas

Prade

Richard

2018

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

The classification of a new item may be viewed as a matter of associating it with the class where it is the least at odds w.r.t. the elements already in the class. An oddness measure of an item with respect to a multiset, applicable to Boolean features as well as to numerical ones, has been recently proposed. It has been shown that cumulating this measure over pairs or triples (rather than larger subsets) of elements in a class could provide an accurate estimate of the global oddness of an item with respect to a class. This idea is confirmed and refined in the present paper. Rather than considering all the pairs in a class, one can only deal with the pairs whose an element is one of the nearest neighbors of the item, in the target class. The oddness evaluation computed on this basis still leads to good results in terms of accuracy. One can take a step further and choose the second element in the pair also as another nearest neighbor in the class. Although the method relies on the notion of neighbors, the resulting algorithm is far from being a variant of the classical k-nearest neighbors approach. The oddness with respect to a class computed only on the basis of pairs made of two nearest neighbors leads to a low complexity algorithm. Experiments on a set of UCI benchmarks show that the classifier obtained can compete with other well-known approaches.

show abstract

“…">

O d d_{2} (N N, S t d)

O d d_{2} (N N, S t d)

has close classification results to those of analogy‐based classifier in the numerical case for most datasets. In the Boolean case, both

o d d n e s s

‐based and

a n a l o g y

‐based classifiers achieve good results for “Balance,” “Car,” “Monk1,” and “Monk3.” For “Monk2” data set, Analogy‐based classifier significantly outperforms

O d d_{2} (N N, S t d)

while for “Spect” and “Voting” the converse is observed.…”

Section: Experimentations and Preliminary Discussionmentioning

confidence: 68%

“…

O d d_{2} (N N, S t d)

has close classification results to those of analogy‐based classifier in the numerical case for most datasets. In the Boolean case, both

o d d n e s s

‐based and

a n a l o g y

‐based classifiers achieve good results for “Balance,” “Car,” “Monk1,” and “Monk3.” For “Monk2” data set, Analogy‐based classifier significantly outperforms

O d d_{2} (N N, S t d)

Section: Experimentations and Preliminary Discussionmentioning

confidence: 81%

See 1 more Smart Citation

Oddness-based classification: A new way of exploiting neighbors

Bounhas

Prade

Richard

2018

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then analogical proportion-based inference [4,20] is usually defined by the following pattern of plausible inference…”

Section: Background On Analogical Proportionsmentioning

confidence: 99%

“…The notion of analogical proportions and their formalization has raised a trend of interest in the last two decades [14,16,18,20,21]. Moreover analogical proportion-based classifiers have been designed and experienced with success [3,4,15], first for Boolean and then for nominal and numerical attributes. In this case, the predicted mark is the label of the class.…”

Section: Introductionmentioning

confidence: 99%

Predicting Preferences by Means of Analogical Proportions

Bounhas¹,

Pirlot

Prade

2018

Case-Based Reasoning Research and Development

Self Cite

View full text Add to dashboard Cite

Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Bounhas, Myriam and Pirlot, Marc and Prade, Henri Predicting preferences by means of analogical proportions. (Abstract. It is assumed that preferences between two items, described in terms of criteria values belonging to a finite scale, are known for a limited number of pairs of items, which constitutes a case base. The problem is then to predict the preference between the items of a new pair. A new approach based on analogical proportions is presented. Analogical proportions are statements of the form "a is to b as c is to d". If the change between item-1 and item-2 is the same as the change between item-3 and item-4, and a similar statement holds for item'-1, item'-2, item'-3, item'-4, then one may plausibly assume that the preference between item-1 and item'-1 is to the preference between item-2 and item'-2 as the preference between item-3 and item'-3 is to the preference between item-4 and item'-4. This offers a basis for a plausible prediction of the fourth preference if the three others are known. This approach fits well with the postulates underlying weighted averages. Two algorithms are proposed that look for triples of preferences appropriate for a prediction. The first one only exploits the given set of examples. The second one completes this set with new preferences deducible from this set under a monotony assumption. This completion is limited to the generation of preferences that are useful for the requested prediction. The predicted preferences should fit with the assumption that known preferences agree with a unique unknown weighted average. The reported experiments suggest the effectiveness of the proposed approach.

show abstract

On the Role of Similarity in Analogical Transfer

Badra

Sedki

Ugon

2018

Case-Based Reasoning Research and Development

View full text Add to dashboard Cite

Analogical transfer consists in making the assumption that if two situations are alike in some respect, they may be alike in others. This paper studies analogical transfer from the viewpoint of measurement theory. We show that analogical transfer can be seen as a similaritybased reasoning on "contour lines" in a qualitative similarity model: if a contour line is included in another at a given point, then it may also be included at other points that are not too (analogically) dissimilar.

show abstract

Analogy-based classifiers for nominal or numerical data

Cited by 37 publications

References 8 publications

Oddness-based classification: A new way of exploiting neighbors

Oddness-based classification: A new way of exploiting neighbors

Predicting Preferences by Means of Analogical Proportions

On the Role of Similarity in Analogical Transfer

Contact Info

Product

Resources

About