Knowledge based decision tree construction with feature importance domain knowledge

Iqbal, Md. Ridwan Al; Rahman, Saiedur; Nabil, Syed Irfan; Chowdhury, Ijaz Ul Amin

doi:10.1109/icece.2012.6471636

Cited by 22 publications

(10 citation statements)

References 2 publications

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“…This algorithm was used in this study on raw reflectance by wavelengths as it is well suited for a large amount of data. Indeed, decision tree algorithms recursively split the data set to learn accurately [64]. ExtraTrees is a machine learning method similar to Random Forest, except that it tends to have a lower variance: instead of searching for the optimal feature/split combination, for each feature, a random value is selected for the split [65].…”

Section: Extra Trees Modelmentioning

confidence: 99%

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Laroche-Pinel

Albughdadi²,

Duthoit³

et al. 2021

Remote Sensing

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The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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Section: Extra Trees Modelmentioning

confidence: 99%

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Laroche-Pinel

Albughdadi²,

Duthoit³

et al. 2021

Remote Sensing

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“…The paper [1] presents the Importance Aided Decision Tree (IADT), which takes feature importance as an additional domain knowledge for enhancing the performance of learners. Decision tree algorithm finds the most important attributes in each node.…”

Section: Related Workmentioning

confidence: 99%

“…The termination condition of the algorithm has three kinds of cases. (1) No attribute can be used as testing attribute.…”

Section: Sprint Algorithmmentioning

confidence: 99%

“…The purpose of classification is to construct a function or a model by which data can be classified into one of the given categories. The classification model can achieve the goal of forecasting data [1,2]. The prediction model is derived from historical data records to represent the trend of the given data, so that it can be used to forecast future data.…”

Section: Introductionmentioning

confidence: 99%

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A Searching Method of Candidate Segmentation Point in SPRINT Classification

Wang¹,

Wang²,

Huo³

et al. 2016

Journal of Electrical and Computer Engineering

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SPRINT algorithm is a classical algorithm for building a decision tree that is a widely used method of data classification. However, the SPRINT algorithm has high computational cost in the calculation of attribute segmentation. In this paper, an improved SPRINT algorithm is proposed, which searches better candidate segmentation point for the discrete and continuous attributes. The experiment results demonstrate that the proposed algorithm can reduce the computation cost and improve the efficiency of the algorithm by improving the segmentation of continuous attributes and discrete attributes.

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“…Furthermore. MARS, MLP, and DTR model also belongs to nonparametric learning, and the model is used in those areas [27][28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modelling Physico-chemical Properties Groundwater in Coastal Plain Area of Vinhlinh and Giolinh Districts of Quangtri Province, Vietnam

Giang¹,

Hieu²,

2021

Preprint

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This paper presents to study the performance of machine learning techniques consisting of Multivariate Adaptive Regression Spline(MARS), Multilayer Perceptron (MLP), and Decision Tree Regression (DTR) for estimating physico-chemical properties groundwater in coastal plain area in Vinhlinh and Giolinh districts of Quangtri province of Vietnam. To deploy the MLP and DTR, different types of transfer and kernel functions were tested, respectively. Determining the results of MARS, MLP and DTR showed that three models have suitable carrying out for forecasting water quality components. Comparison of outcomes of MARS model with MLP, DTR models indicates that this model has good performance for forecasting the elements of water quality, its level of accuracy is slightly more than other. To assess the accurate values of the models according to the measurement parameters indicated that order models were MARS, DTR, and MLP, respectively.

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Knowledge based decision tree construction with feature importance domain knowledge

Cited by 22 publications

References 2 publications

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

A Searching Method of Candidate Segmentation Point in SPRINT Classification

Predictive Modelling Physico-chemical Properties Groundwater in Coastal Plain Area of Vinhlinh and Giolinh Districts of Quangtri Province, Vietnam

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