Representing conditional preference by boosted regression trees for recommendation

Liu, Juntao; Sui, Chenhong; Deng, Dewei; Wang, Junwei; Feng, Bin; Liu, Wenyu; Wu, Caihua

doi:10.1016/j.ins.2015.08.001

Cited by 39 publications

(14 citation statements)

References 16 publications

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“…BRT is flexible to use and yet capable of dealing with complex soil-environmental responses, including interactions and nonlinearities [22,27]. Compared to the traditional regression approach, BRT showed better results in terms of prediction capability [40,41]. BRT performed well in soil properties modeling [42] and has been applied in various studies [27,43].…”

Section: Prediciton Model Based On Boosted Regression Treesmentioning

confidence: 99%

Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Wang

Adhikari

et al. 2016

Sustainability

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Soil organic carbon (SOC) plays an important role in soil fertility and the global carbon cycle. A better understanding of spatial-temporal changes of SOC content is essential for soil resource management, emission studies, and carbon accounting. In this study, we used a boosted regression trees (BRT) model to map distributions of SOC content in the topsoil (0-20 cm) and evaluated its temporal dynamics from 1990-2010 in Wafangdian City, northeast of China. A set of 110 (1990) and 127 (2010) soil samples were collected and nine environment variables (including topography and vegetation) were used. A 10-fold cross-validation was used to evaluate model performance as well as predictive uncertainty. Accuracy assessments showed that R 2 of 0.53 and RMSE (Root-mean-square error) of 9.7 g·kg −1 for 1990, and 0.55, and 5.2 g·kg −1 for 2010. Elevation and NDVI (Normalized Difference Vegetation Index) were the two important variables affecting SOC distribution. Results showed that mean SOC content decreased from 19 ± 14 to 18 ± 8 g·kg −1 over a 20 year period. The maps of SOC represented a decreasing trend from south to north across the study area in both periods. Rapid urbanization and land-use changes were accountable for declining SOC levels. We believe predicted maps of SOC can help local land managers and government agencies to evaluate soil quality and assess carbon sequestration potential and carbon credits.

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Section: Prediciton Model Based On Boosted Regression Treesmentioning

confidence: 99%

Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Wang

Adhikari

et al. 2016

Sustainability

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“…Although regression trees are not as popular as classification trees, they are highly competitive with different machine learning algorithms (Ortuno et al 2015) and are often applied to many real-life problems (Fakhari and Moghadam 2013;Liu et al 2016).…”

Section: Decision Treesmentioning

confidence: 99%

A multi-objective evolutionary approach to Pareto-optimal model trees

Czajkowski

Krȩtowski

2018

Soft Comput

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This paper discusses the multi-objective evolutionary approach to induction of model trees. The model tree is a particular case of a decision tree designed to solve regression problems. Although the decision tree induction is inherently a multiobjective task, most of the conventional learning algorithms can only deal with a single objective that may possibly aggregate multiple objectives. The goal of this paper is to demonstrate how a set of non-dominated model trees can be obtained using the Global Model Tree (GMT) system. The GMT framework can be used for the evolutionary induction of different types of decision trees, including univariate, oblique or mixed; regression and model trees. Proposed Pareto approach for GMT allows the decision maker to select desired output model according to his preferences on the conflicting objectives. Performed study covers the regression trees and the model trees with two or three objectives that relate to the tree error and the tree comprehensibility. Experimental evaluation discusses the importance of multi-objective components like crowding function and archive elitist selection, using real-life datasets. Finally, the presented multi-objective GMT solution is confronted with competitive regression and model tree inducers.

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“…Because the coefficients in the high-frequency wavelet subbands are sparse, we can divide the subbands into several blocks and then check whether they contain significant coefficients. The tree structure is widely used in many fields of information sciences [30,31,32], and is also a good model for compression [33]. In [7], each block is split into four sub-blocks once it tests as significant with respective to the current threshold.…”

Section: The Btca Algorithmmentioning

confidence: 99%

Remote sensing image compression based on binary tree and optimized truncation

Huang

Ren

et al. 2017

Digital Signal Processing

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The remote sensing image data is so vast that it requires compression by low-complexity algorithm on space-borne equipment. Binary tree coding with adaptive scanning order (BTCA) is an effective algorithm for the mission. However, for large-scale remote sensing images, BTCA requires a lot of memory, and does not provide random access property. In this paper, we propose a new coding method based on BTCA and optimize truncation. The wavelet image is first divided into several blocks which are encoded individually by BTCA. According the property of BTCA, we select the valid truncation points for each block carefully to optimize the ratio of rate-distortion, so that a higher compression ratio, lower memory requirement and random access property are attained. Without any entropy coding, the proposed method is simple and fast, which is very suitable for space-borne equipment. Experiments are conducted on three remote sensing image sets, and the results show that it can significantly improve PSNR, SSIM and VIF, as well as subjective visual experience.

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Representing conditional preference by boosted regression trees for recommendation

Cited by 39 publications

References 16 publications

Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China

A multi-objective evolutionary approach to Pareto-optimal model trees

Remote sensing image compression based on binary tree and optimized truncation

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