A combined nonparametric approach to feature selection and binary decision tree design
References (13)
- et al.
Quantization complexity and independent measurements
IEEE Trans. Comput.
(1974) - et al.
A nonparametric partitioning procedure for pattern classification
IEEE Trans. Comput.
(1969) - et al.
The decision tree classifier: design and potential
Some current concepts and problems in pattern classification and feature extraction
A recursive partitioning decision rule for nonparametric classification
IEEE Trans. Comput.
(1977)K-S test for detecting changes from Landsat imagery data
IEEE Trans. Systems, Man Cybernet.
(1979)
Cited by (74)
Towards improving decision tree induction by combining split evaluation measures
2023, Knowledge-Based SystemsMethod selection in short-term eruption forecasting
2021, Journal of Volcanology and Geothermal ResearchCitation Excerpt :A decision tree is a collection of conditions (internal nodes) from which branches split; the decision is any branch without a condition at the end. The aim of a decision tree is to have as few branches as possible while classifying the data as accurately as possible (Rounds, 1980). Different types of trees include, for example, classification trees, which have decisions based on categorical data (e.g., eruption / no eruption), and regression trees which carry continuous values (e.g., probability of eruption).
Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes
2018, TectonophysicsCitation Excerpt :Due to the limited amount of available data, we performed only the learning phase aiming at identifying any recurrent pattern that discriminates segments that have experienced GEqs from those that have not. The analysis was based on two different PR non-parametric algorithms: the Binary Decision Tree BDT (Mulargia et al., 1992; Rounds, 1980) and the Fisher discriminant analysis FIS (e.g., Duda and Hart, 1973). Both algorithms have been previously used on synthetic data with known patterns to test their ability of recognizing recurrent schemes and extracting relevant features on small datasets, with non-normal and discrete/categorical parameters (e.g., Sandri and Marzocchi, 2004).
Climate-driven endemic cholera is modulated by human mobility in a megacity
2017, Advances in Water ResourcesCitation Excerpt :Following the methodology in Reiner et al. (2012), the optimal outbreak probability threshold retained for prediction was found using the Kolmogorov–Smirnov test. The threshold corresponds to the point of maximum distance between the Cumulative Mass Functions of the predicted outbreak probability in months where an outbreak did and did not occur (Rounds, 1980). Given the identified optimal threshold, the confusion matrix of observed and predicted outbreak was used a basis for the computation of predictive performance, including accuracy and false positive and negative rates.
Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery
2016, International Journal of Applied Earth Observation and GeoinformationCitation Excerpt :Values of dKS ≃ 1 indicate high separability while identical spectra are characterized by dKS close to 0. This measure has been frequently applied in remote sensing for feature selection, class separability assessment and multi-temporal change detection applications or geometric accuracy assessment (Rounds, 1980; Möller et al., 2012; Tang et al., 2011; Möller et al., 2013). A practical difference of the two calculated spectral separability indicators is that η2 can potentially be used for a multi-class classification scenario, while the Kolmogorov–Smirnov distance can only be used to evaluate spectral differences of two different classes.
Enhancing the Accuracy of Land Cover Classification by Airborne LiDAR Data and WorldView-2 Satellite Imagery
2022, ISPRS International Journal of Geo-Information
- ∗
Author now at the Aerospace Corporation, Mail Station A2-1213, P.O. Box 92957 Los Angeles, CA 90009, U.S.A.