Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure
This paper introduces the first generic version of data dependent dissimilarity and shows
that it provides a better closest match than distance measures for three existing algorithms in …
that it provides a better closest match than distance measures for three existing algorithms in …
Data-dependent dissimilarity measure: an effective alternative to geometric distance measures
Nearest neighbor search is a core process in many data mining algorithms. Finding reliable
closest matches of a test instance is still a challenging task as the effectiveness of many …
closest matches of a test instance is still a challenging task as the effectiveness of many …
Forest-type regression with general losses and robust forest
AH Li, A Martin - International conference on machine …, 2017 - proceedings.mlr.press
This paper introduces a new general framework for forest-type regression which allows the
development of robust forest regressors by selecting from a large family of robust loss …
development of robust forest regressors by selecting from a large family of robust loss …
RatioRF: a novel measure for random forest clustering based on the Tversky's ratio model
M Bicego, F Cicalese, A Mensi - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this paper we propose, a novel Random Forest-based similarity measure for clustering.
We build upon Tversky's ratio model definition of similarity [1] and specialize it to the …
We build upon Tversky's ratio model definition of similarity [1] and specialize it to the …
A new simple and effective measure for bag-of-word inter-document similarity measurement
To measure the similarity of two documents in the bag-of-words (BoW) vector representation,
different term weighting schemes are used to improve the performance of cosine similarity …
different term weighting schemes are used to improve the performance of cosine similarity …
Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms
The use of distance metrics such as the Euclidean or Manhattan distance for nearest
neighbour algorithms allows for interpretation as a geometric model, and it has been widely …
neighbour algorithms allows for interpretation as a geometric model, and it has been widely …
A comparative study of data-dependent approaches without learning in measuring similarities of data objects
Conventional general-purpose distance-based similarity measures, such as Minkowski
distance (also known as ℓ _p ℓ p-norm with p> 0 p> 0), are data-independent and sensitive …
distance (also known as ℓ _p ℓ p-norm with p> 0 p> 0), are data-independent and sensitive …
A Fuzzy Twin Support Vector Machine Based on Dissimilarity Measure and Its Biomedical Applications
J Qiu, J Xie, D Zhang, R Zhang, M Lin - International Journal of Fuzzy …, 2024 - Springer
Biomedical data exhibit high-dimensional complexity in its internal structure and are
susceptible to noise interference, making classification tasks in biomedical data highly …
susceptible to noise interference, making classification tasks in biomedical data highly …
On the Good Behaviour of Extremely Randomized Trees in Random Forest-Distance Computation
M Bicego, F Cicalese - Joint European Conference on Machine Learning …, 2023 - Springer
Originally introduced in the context of supervised classification, ensembles of Extremely
Randomized Trees (ERT) have shown to provide surprisingly effective models also in …
Randomized Trees (ERT) have shown to provide surprisingly effective models also in …
Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity
Symbolic event recognition systems detect event occurrences using first-order logic rules.
Although existing online structure learning approaches ease the discovery of such rules in …
Although existing online structure learning approaches ease the discovery of such rules in …