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Ordinal classification for interval-valued data and interval-valued functional data [PDF]
Expert Systems with Applications, 2023 The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For the first time, interval-valued data and interval-valued functional data are considered as inputs in an ordinal classification problem.
Aleix Alcacer +2 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In the process of land cover segmentation from remote sensing image, there are some uncertainties such as “significant difference in class density”, “different objects with same spectrum”, and “same object with ...
Chengmao Wu, Xiaokang Guo
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AN IMPROVED METHODOLOGY FOR MULTI-CRITERIA EVALUATIONS IN THE SHIPPING INDUSTRY
Brodogradnja, 2016 This paper presents a reliable, easy and more objective approach for ranking and determining preference in a multi-criteria decision-making problem within the shipping industry.
Daniel Osezua Aikhuele +1 more
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Linear regression analysis for interval‐valued functional data
Stat, 2021 Recent advances in information technology have led to the appearance of high‐dimensional and complex data sets which necessitates in‐depth investigation on high‐dimensional data analysis and modelling. The current study introduces five approaches to fit a functional linear regression model on interval‐valued functional data. The proposed approaches are
Roya Nasirzadeh +2 more
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On central tendency and dispersion measures for intervals and hypercubes [PDF]
, 2008 The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall.
Bertrand P. +9 more
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Gradient extraction operators for discrete interval-valued data [PDF]
, 2015 Digital images are generally created as discrete measurements of light, as performed by dedicated sensors. Consequently, each pixel contains a discrete approximation of the light inciding in a sensor element.
Bustince, Humberto +4 more
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Interval-Valued q-Rung Orthopair Fuzzy QUALIFLEX Decision Analysis Method with Dombi Operators
Discrete Dynamics in Nature and Society, 2022 As a new and useful decision tool, interval-valued q-rung orthopair fuzzy set (IVq-ROFS), has strong ability to address uncertain or imprecision information in practical decision-making processes.
Benting Wan +3 more
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Constrained Regression for Interval-Valued Data [PDF]
Journal of Business & Economic Statistics, 2013 Current regression models for interval-valued data do not guarantee that the predicted lower bound of the interval is always smaller than its upper bound. We propose a constrained regression model that preserves the natural order of the interval in all instances, either for in-sample fitted intervals or for interval forecasts.
Gloria González-Rivera, Wei Lin
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Asymptotically Efficient Estimation of Weighted Average Derivatives with an Interval Censored Variable [PDF]
, 2013 This paper studies the identification and estimation of weighted average derivatives of conditional location functionals including conditional mean and conditional quantiles in settings where either the outcome variable or a regressor is interval-valued.
Kaido, Hiroaki
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Feature Selection for Interval-Valued Data Based on D-S Evidence Theory
IEEE Access, 2021 Feature selection is one basic and critical technology for data mining, especially in current “big data era”. Rough set theory (RST) is sensitive to noise in feature selection due to the strict condition of equivalence relation.
Yichun Peng, Qinli Zhang
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