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Supervised non-parametric discretization based on Kernel density estimation
Flores, J.L.; Calvo, B.; Pérez, A.
(2019-12-19)
(2019-12-19)
Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main ...
Soft information for localization-of-things
Conti, A.; Mazuelas, S.; Bartoletti, S.; Lindsey, W.C; Win, M. (2019-11-01)
; Bartoletti, S.; Lindsey, W.C; Win, M. (2019-11-01)
Location awareness is vital for emerging Internetof-
Things applications and opens a new era for Localizationof-
Things. This paper first reviews the classical localization
techniques based on single-value metrics, such ...
An evolutionary discretized Lambert approach for optimal long-range rendezvous considering impulse limit
Shirazi, A.; Ceberio, J.; Lozano, J.A. (2019-09-18)
; Ceberio, J.; Lozano, J.A. (2019-09-18)
In this paper, an approach is presented for finding the optimal long-range space rendezvous in terms of fuel and time, considering limited impulse. In this approach , the Lambert problem is expanded towards a discretized ...
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework
Carreño, A.; Inza, I.; Lozano, J.A. (2019-09-01)
; Inza, I.; Lozano, J.A. (2019-09-01)
In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a ...
Crowd-Centric Counting via Unsupervised Learning
Morselli, F.; Bartoletti, S.; Mazuelas, S.; Win, M.; Conti, A. (2019-07-11)
; Win, M.; Conti, A. (2019-07-11)
Counting targets (people or things) within a moni-tored area is an important task in emerging wireless applications,including those for smart environments, safety, and security.Conventional device-free radio-based ...
Optimal multi-impulse space rendezvous considering limited impulse using a discretized Lambert problem combined with evolutionary algorithms
Shirazi, A.; Ceberio, J.; Lozano, J.A. (2019-07-01)
; Ceberio, J.; Lozano, J.A. (2019-07-01)
In this paper, a direct approach is presented to tackle the multi-impulse rendezvous problem considering the impulse limit. Particularly, the standard Lambert problem is extended toward several consequential orbit transfers ...
Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance
Arza, E.; Ceberio, J.; Pérez, A.; Irurozki, E. (2019-07)
; Ceberio, J.; Pérez, A.; Irurozki, E. (2019-07)
The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ...
A mathematical analysis of edas with distance-based exponential models
Unanue, I.; Merino, M.; Lozano, J.A. (2019-07-01)
; Lozano, J.A. (2019-07-01)
Estimation of Distribution Algorithms have been successfully used for solving many combinatorial optimization problems. One type of problems in which Estimation of Distribution Algorithms have presented strong competitive ...
K-means for massive data
(2019-04-30)
The $K$-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due to its easiness in the implementation, straightforward parallelizability
and competitive computational complexity, when ...
Early classification of time series using multi-objective optimization techniques
Mori, U.; Mendiburu, A.; Miranda, I.M.; Lozano, J.A. (2019-04-23)
; Mendiburu, A.; Miranda, I.M.; Lozano, J.A. (2019-04-23)
In early classification of time series the objective is to build models which are able to make class-predictions for time series as accurately and as early as possible, when only a part of the series is available. It is ...