Development of an entropy-based feature selection method and analysis of online reviews on real estate

Horino, Hiroki; Nonaka, Hirofumi; Carreón, Elisa Claire Alemán; Hiraoka, Toru

doi:10.1109/ieem.2017.8290312

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…In another study, the linear regression method and the gradient boosting approach are compared for the modelling of the property prices in China considering 253 units where it is demonstrated that the gradient boosting method is more accurate [10]. The SVM methodology is applied for the modelling of the housing prices in Japan for 6320631 advertisements where it is shown that the SVM approach is an effective method for the property price modelling [11]. The three types of regression namely the linear regression, multivariate regression and the polynomial regression are utilized for the modelling of the housing prices in India considering 21000 properties and it is found out that the mixture of these three methods performs the best [12].…”

Section: Literature Surveymentioning

confidence: 99%

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Tuncsiper

2024

CJMS

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The real estate market in Türkiye has witnessed significant growth and complexity in recent years necessitating advanced modelling techniques for accurate property price predictions. This paper presents a comprehensive study on the application of deep learning networks to model unit property prices across Türkiye, with a specific emphasis on its three major cities: Istanbul, Ankara, and Izmir. The unit property prices in the overall country and its three major cities are taken from the official resources and then the square meter unit property prices in USD are obtained. Then, a deep learning network is developed in Python programming language which accepts the lagged values of the unit property prices as input values. Then, the deep learning network is trained using the datasets separately for Türkiye and the three major cities. The loss curves of the deep learning network show a rapid convergence indicating the suitability of the developed deep learning network for the unit property price modelling. The actual and the modelled data are plotted indicating the accuracy of the developed model. Finally, the performance indicators of the developed deep learning model namely the coefficient of determination, mean absolute error, root mean square error and the mean absolute percentage error are computed verifying the high accuracy with R2 values greater than 0.95 for all four modelling cases. The alternative utilization opportunities of the proposed deep learning model are also discussed in the conclusions section.

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Section: Literature Surveymentioning

confidence: 99%

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Tuncsiper

2024

CJMS

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“…There are two categories of problemsolving using machine learning namely supervised learning and unsupervised learning algorithms (Fiorucci & James, 2020). Between the two, the most commonly used is the supervised learning algorithm specifically for predicting ϒ (Horino & Nonaka, 2017). The supervised learning algorithm is used for predicting the outcome of a given input; it uses the examples of the input or output pairs and requires human effort to create a training set for building the machine learning algorithm process (Jordan & Mitchell, 2015).…”

Section: Machine Learningmentioning

confidence: 99%

“…Random Forest is also known as an ensemble learning which can be used in classification and regression problem methods; one of its advantages is protecting against overfitting which in turn improves performance(Sabbeh, 2018). It is usually used as a decision-making tool in real estate specifically for predicting the price of housing(Horino & Nonaka, 2017). Random Forest has a Decision Tree collection known as "Forest", but Random Forest generalizes better than Decision Tree towards improving accuracy i.e., by selecting the highest votes(Fiorucci & James, 2020).Next is the Linear Regression algorithm or better known as ordinary least square OLS(Varma & Sarma, 2018).…”

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confidence: 99%

Application of Machine Learning in Analysing Historical and Non-Historical Characteristics of Heritage Pre-War Shophouses

Ja’afar

Mohamad

2021

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Real estate is complex and its value is influenced by many characteristics. However, the current practice in Malaysia shows that historical characteristics have not been given primary consideration in determining the value of heritage properties. Thus, the accuracy of the values produced is questionable. This paper aims to determine whether the historical characteristics of the pre-war shophouses at North-East Penang Island, Malaysia contribute any significance to their value. Several Machine Learning algorithms have been developed for this purpose namely Random Forest, Decision Tree, Lasso Regression, Ridge Regression and Linear Regression. The result shows that the Random Forest Regressor with historical characteristics is the best fitting model with higher values of R-squared (R²) and lowest value of Root Mean Square Error (RMSE). This indicates that the historical characteristics of the heritage property under study contribute to its significant value. By considering the historical characteristics, the property’s value can be better predicted.

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Predicting Student Performance from Online Engagement Activities Using Novel Statistical Features

Brahim

2022

Arab J Sci Eng

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Predicting students’ performance during their years of academic study has been investigated tremendously. It offers important insights that can help and guide institutions to make timely decisions and changes leading to better student outcome achievements. In the post-COVID-19 pandemic era, the adoption of e-learning has gained momentum and has increased the availability of online related learning data. This has encouraged researchers to develop machine learning (ML)-based models to predict students’ performance during online classes. The study presented in this paper, focuses on predicting student performance during a series of online interactive sessions by considering a dataset collected using digital electronics education and design suite. The dataset tracks the interaction of students during online lab work in terms of text editing, a number of keystrokes, time spent in each activity, etc., along with the exam score achieved per session. Our proposed prediction model consists of extracting a total of 86 novel statistical features, which were semantically categorized in three broad categories based on different criteria: (1) activity type, (2) timing statistics, and (3) peripheral activity count. This set of features were further reduced during the feature selection phase and only influential features were retained for training purposes. Our proposed ML model aims to predict whether a student’s performance will be low or high. Five popular classifiers were used in our study, namely: random forest (RF), support vector machine, Naïve Bayes, logistic regression, and multilayer perceptron. We evaluated our model under three different scenarios: (1) 80:20 random data split for training and testing, (2) fivefold cross-validation, and (3) train the model on all sessions but one which will be used for testing. Results showed that our model achieved the best classification accuracy performance of 97.4% with the RF classifier. We demonstrated that, under similar experimental setup, our model outperformed other existing studies.

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Development of an entropy-based feature selection method and analysis of online reviews on real estate

Cited by 5 publications

References 12 publications

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Application of Machine Learning in Analysing Historical and Non-Historical Characteristics of Heritage Pre-War Shophouses

Predicting Student Performance from Online Engagement Activities Using Novel Statistical Features

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