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Found 18 papers, 4 papers with code
LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm Detection
This paper describes the approach developed by the LT3 team in the Intended Sarcasm Detection task at SemEval-2022 Task 6.
Fuzzy Rough Choquet Distances for Classification
This paper introduces a novel Choquet distance using fuzzy rough set based measures.
FRRI: a novel algorithm for fuzzy-rough rule induction
In this paper, we introduce a novel rule induction algorithm called Fuzzy Rough Rule Induction (FRRI).
On the Granular Representation of Fuzzy Quantifier-Based Fuzzy Rough Sets
The main findings reveal that Choquet-based fuzzy rough sets can be represented granularly under the same conditions as OWA-based fuzzy rough sets, whereas Sugeno-based FRS can always be represented granularly.
A unified weighting framework for evaluating nearest neighbour classification
NN and FRNN perform best with a combination of Samworth rank- and distance weights and scaling by the mean absolute deviation around the median ($r_1$), the standard deviaton ($r_2$) or the interquartile range ($r_{\infty}^*$), while FNN performs best with only Samworth distance-weights and $r_1$- or $r_2$-scaling.
Classifying token frequencies using angular Minkowski $p$-distance
Angular Minkowski $p$-distance is a dissimilarity measure that is obtained by replacing Euclidean distance in the definition of cosine dissimilarity with other Minkowski $p$-distances.
Fuzzy Rough Sets Based on Fuzzy Quantification
In this paper, we improve on VQFRS by introducing fuzzy quantifier-based fuzzy rough sets (FQFRS), an intuitive generalization of fuzzy rough sets that makes use of general unary and binary quantification models.
Evaluation of the impact of the indiscernibility relation on the fuzzy-rough nearest neighbours algorithm
In this paper, we investigate the impact of this indiscernibility relation on the performance of FRNN classification.
No imputation without representation
Imputation allows datasets to be used with algorithms that cannot handle missing values by themselves.
Fuzzy granular approximation classifier
At the end, we discuss the transparency of the FGAC and its advantage over other locally transparent methods.
Choquet-Based Fuzzy Rough Sets
In classical fuzzy rough sets, the lower and upper approximations are determined using the minimum and maximum operators, respectively.
Multi-class granular approximation by means of disjoint and adjacent fuzzy granules
In granular computing, fuzzy sets can be approximated by granularly representable sets that are as close as possible to the original fuzzy set w. r. t.
A Novel Machine Learning Approach to Data Inconsistency with respect to a Fuzzy Relation
Inconsistency in prediction problems occurs when instances that relate in a certain way on condition attributes, do not follow the same relation on the decision attribute.
Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets
Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition.
Nearest neighbour approaches for Emotion Detection in Tweets
Emotion detection is an important task that can be applied to social media data to discover new knowledge.
Optimised one-class classification performance
The hyperparameters of SVM and LOF have to be optimised through cross-validation, while NND, LNND and ALP allow an efficient form of leave-one-out validation and the reuse of a single nearest-neighbour query.
Average Localised Proximity: A new data descriptor with good default one-class classification performance
One-class classification is a challenging subfield of machine learning in which so-called data descriptors are used to predict membership of a class based solely on positive examples of that class, and no counter-examples.
