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Automatic Feature Scaling and Selection for Support Vector Machine Classi cation with Functional Data
(Springer, 2020)
Functional Data Analysis (FDA) has become a very important eld
in recent years due to its wide range of applications. However, there are several
real-life applications in which hybrid functional data appear, i.e., data ...
Feature selection for Support Vector Machines via Mixed Integer Linear Programming
(Elsevier, 2014)
The performance of classification methods, such as Support Vector Machines, depends
heavily on the proper choice of the feature set used to construct the classifier. Feature
selection is an NP-hard problem that has been ...
Profit-based feature selection using support vector machines - General framework and an application for customer retention
(Elsevier, 2015)
Churn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral ...
SVR-FFS: A novel forward feature selection approach for high-frequency time series forecasting using support vector regression
(Elsevier, 2020)
n this paper, we propose a novel support vector regression (SVR) approach for time series analysis. An efficient forward feature selection strategy has been designed for dealing with high-frequency time series with multiple ...
Prevalence, infected density or individual probability of infection? Assessing vector infection risk in the wild transmission of Chagas disease
(The Royal Society, 2020)
Vector-borne infectious disease dynamics result mainly from the intertwined effect of the diversity, abundance, and behaviour of hosts and vectors. Most studies, however, have analysed the relationship between host-species ...
Advanced conjoint analysis using feature selection via support vector machines
(Elsevier, 2015)
One of the main tasks of conjoint analysis is to identify consumer preferences about potential products or services. Accordingly, different estimation methods have been proposed to determine the corresponding relevant ...
Simultaneous feature selection and heterogeneity control for SVM classification: an application to mental workload assessment
(Elsevier, 2020)
In this study, an expert system is presented for analyzing the mental workload of interacting with a mobile phone while facing common daily tasks. Psychophysiological signals were collected from various devices, each ...
Embedded heterogeneous feature selection for conjoint analysis: A SVM approach using L1 penalty
(Springer, 2017)
This paper presents a novel embedded feature selection approach for Support Vector Machines (SVM) in a choice-based conjoint context. We extend the L1-SVM formulation and adapt the RFE-SVM algorithm to conjoint analysis ...
Lizards and rabbits may increase Chagas infection risk in the Mediterranean-type ecosystem of South America
(Nature, 2020)
Studies of host-parasite relationships largely benefit from adopting a multifactorial approach, including the complexity of multi-host systems and habitat features in their analyses. Some host species concentrate most ...
Iris recognition using low-level CNN layers without training and single matching
(IEEE, 2022)
Iris is one of the most accurate biometrics. This has led to the successful development of large-scale applications. However, with population growth, and new international applications, datasets are constantly increasing ...