Adaptive Pattern Recognition with Random Costs and Its Application to Decision Trees

Dattatreya, G.R.; Kanal, Laveen N.

doi:10.1109/tsmc.1986.4308941

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…actions at the levels below the present level are not known. Dattatreya and Kanal [13] propose an interesting unsupervised scheme that adaptively learns the mean values of these random variables and improves the tree performance.…”

Section: Iv3 Decision Rules and Search Strategies In Dtc'smentioning

confidence: 99%

A survey of decision tree classifier methodology

Safavian

Landgrebe

1991

IEEE Trans. Syst., Man, Cybern.

3,158

1,227

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Section: Iv3 Decision Rules and Search Strategies In Dtc'smentioning

confidence: 99%

A survey of decision tree classifier methodology

Safavian

Landgrebe

1991

IEEE Trans. Syst., Man, Cybern.

3,158

1,227

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“…The decision tree classifier (DTC) (Dattatreya and Kanal, 1986;Safavian and Landgrebe, 1991) is one of the methods for multi-stage decision making. The basic principle involved in any multistage approach is to break up a complex decision into a union of several simpler decisions, hoping the final solution obtained this way would resemble the intended desired solution.…”

Section: Introductionmentioning

confidence: 99%

Classification and Evolutionary Analysis of Yellow River Delta Wetlands Using Decision Tree Based on Time Series SAR Backscattering Coefficient and Coherence

Wang

Liu

et al. 2022

Front. Mar. Sci.

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In recent years, the Yellow River Delta has been affected by invasive species Spartina alterniflora (S. alterniflora), resulting in a fragile ecological environment. It is of great significance to monitor the ground object types in the Yellow River Delta wetlands. The classification accuracy based on Synthetic Aperture Radar (SAR) backscattering coefficient is limited by the small difference between some ground objects. To solve this problem, a decision tree classification method for extracting the ground object types in wetland combined time series SAR backscattering and coherence characteristics was proposed. The Yellow River Delta was taken as the study area and the 112 Sentinel-1A GRD data with VV/VH dual-polarization and 64 Sentinel-1A SLC data with VH polarization were used. The decision tree method was established, based on the annual mean VH and VV backscattering characteristics, the new constructed radar backscattering indices, and the annual mean VH coherence characteristics were suitable for extracting the wetlands in the Yellow River Delta. Then the classification results in the Yellow River Delta wetlands from 2018 to 2021 were obtained using the new method proposed in this paper. The results show that the overall accuracy and Kappa coefficient of the proposed method w5ere 89.504% and 0.860, which were 9.992% and 0.127 higher than multi-temporal classification by Support Vector Machine classifier. Compared with the decision tree without coherence, the overall accuracy and Kappa coefficient were improved by 8.854% and 0.108. The spatial distributions of wetland types in the Yellow River Delta from 2018 to 2021 were obtained using the constructed decision tree. The spatio-temporal evolution analysis was conducted. The results showed that the area of S. alterniflora decreased significantly in 2020 but it increased to the area of 2018 in 2021. In addition, S. alterniflora seriously affected the living space of Phragmites australis (P. australis) and in 4 years, 10.485 km2 living space of P. australis was occupied by S. alterniflora. The proposed method can provide a theoretical basis for higher accuracy SAR wetland classification and the monitoring results can provide an effective reference for local wetland protection.

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