An Ensemble Learning Based Model for Real Estate Project Classification

Paireekreng, Worapat; Choensawat, Worawat

doi:10.1016/j.promfg.2015.07.892

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…A comparison of various ensemble techniques to increase prediction accuracy (Graczyk et al, 2010) led to a conclusion that, in general, property valuation results obtained with stacking of utilized models were characterized by the lowest prediction error but the outcome tended to vary. Ensemble techniques were also used on real estate market for projects classification (Paireekreng and Choensawat, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets*

Gnat¹,

Doszyń²

2020

ERSJ

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Section: Literature Reviewmentioning

confidence: 99%

Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets*

Gnat¹,

Doszyń²

2020

ERSJ

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“…Weights are updated for wrong inputs and recursively applied to improve the accuracy. A k-NN based classifier is proposed by Choensawat et al [11] for identifying novelty class and outliers in real estate project. Snehalata et al [12] proposed a hybrid approach known as HEDDM which reacts to both type of drift, sudden and gradual drift.…”

Section: Related Workmentioning

confidence: 99%

Impact of Variable Size Chunk Data on Classifier Performance using Ensemble Techniques

Deshpande¹

2020

IJATCSE

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In recent times, enormous growth of real-world data and its usage raised an issue of processing data for extracting meaningful patterns. Due to huge volumes and diversification in the data, traditional knowledge of data mining algorithms lower down the accuracy. Due to data drift, a good accuracy model may not outperform for generalized data. So, it is necessary to handle the causes of drift and its impact on the model accuracy. Existing approaches use a fixed size sliding window approach. In contrast, our approach uses both fixed window and adaptive window approach to detect the concept drift. We have used maximum likelihood estimation technique. CUSUM chart ,Simple Moving Average and a cross correlation technique to detect a change in the concept . We have analyzed the impact of variable size chunk data on different ensemble model. Our approach improves the classifier accuracy using better feature selection and evolution method. The combine approach of variable sized sample and weighted Ensemble classifiers not only detect the change in the concept but also applied for drift detection. Under data drift strategy, we inclusively compare the classifiers performance on electricity dataset ,refereed by research community .

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