Forecasting house price index of China using dendritic neuron model

Yu, Ying; Song, Shuangbao; Zhou, Tianle; Yachi, Hanaki; Gao, Shangce

doi:10.1109/pic.2016.7949463

Cited by 19 publications

(5 citation statements)

References 11 publications

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“…House price prediction can be divided into two categories, first by focusing on house characteristics, and secondly by focusing on the model used in house price prediction. Many researchers have produced a house price prediction model, including [1,3,[6][7][8].…”

Section: Attributesmentioning

confidence: 99%

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

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Section: Attributesmentioning

confidence: 99%

House Price Prediction using a Machine Learning Model: A Survey of Literature

Zulkifley¹,

Rahman²,

Ubaidullah³

et al. 2020

IJMECS

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“…Its effectiveness in classification, approximation, and prediction has been demonstrated in various studies, including refs. [17, 19–40].…”

Section: Introductionmentioning

confidence: 99%

Pruning method for dendritic neuron model based on dendrite layer significance constraints

Luo

Wen

et al. 2023

CAAI Trans on Intel Tech

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The dendritic neural model (DNM) mimics the non-linearity of synapses in the human brain to simulate the information processing mechanisms and procedures of neurons. This enhances the understanding of biological nervous systems and the applicability of the model in various fields. However, the existing DNM suffers from high complexity and limited generalisation capability. To address these issues, a DNM pruning method with dendrite layer significance constraints is proposed. This method not only evaluates the significance of dendrite layers but also allocates the significance of a few dendrite layers in the trained model to a few dendrite layers, allowing the removal of low-significance dendrite layers. The simulation experiments on six UCI datasets demonstrate that our method surpasses existing pruning methods in terms of network size and generalisation performance.

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“…The House Price Index(HPI) [27,29] is a broad measure and often serves as an indicator for the house price in a given geographical area. This measure is a weighted repeated sales Index, meaning it measures the average price change based on repeated sales.…”

Section: Introductionmentioning

confidence: 99%

Estimate House Price Using Machine Learning

Rishita Gadde,

Md Amiruzzaman,

Richard Burns

et al. 2023

ReBICTE

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The House Price Index (HPI) is commonly used to estimate the changes in housing prices. The sale price of the house is correlated with many other factors like geographical location, size of the house, age of the house, the area and population of the neighborhood etc. There has been a considerably large number of datasets released in the literature of various locations to explore the correlation of the sale price of houses with their corresponding features. However, all the features don’t affect the sale price in equal proportion. Some features strongly correlate with each other and, while some features don’t carry any importance or are probably redundant. As a result, to explore various impacts of features on sales prices, we performed a detailed data analysis on the original house dataset. This report also comprehensively validates multiple steps of data analysis with supporting statistics and visualizations to provide an optimistic result of various features and their impact on the sales price of houses.

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Forecasting house price index of China using dendritic neuron model

Cited by 19 publications

References 11 publications

House Price Prediction using a Machine Learning Model: A Survey of Literature

House Price Prediction using a Machine Learning Model: A Survey of Literature

Pruning method for dendritic neuron model based on dendrite layer significance constraints

Estimate House Price Using Machine Learning

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