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Subject: search.filters.subject.decision tree

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    Prediction of Water Quality Index (WQI) Using Machine Learning
    (2025-01-01)
    Kularbphettong, Kunyanuth
    ;
    Raksuntorn, Nareenart
    ;
    Boonseng, Chongrag
    The purpose of this project is to assess Water Quality Index (WQI) by using five machine learning techniques including the Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). In this case, we are using Thailand as a base country for assessing water quality of the rivers and canals. The data set was collected from Bangkok Metropolitan Authority of Thailand during the period January 2018 to January 2021. The data set included 43,776 records and each record comprised 12 quantitative measurements related to water quality. Hence, they were used as feature inputs of the assessment model; for instance, pH, DO (Dissolved Oxygen), BOD (Biochemical Oxygen Demand), TP (Total Phosphorus), TCB (Total Coloniform Bacteria), FCB (Fecal Coloniform Bacteria), NO3-N (Nitrogen-Nitrogen), No2-N (Nitrogen-Suspended Solid), NH3-N (Ammonia-Nitrogen), TS (Total Solid), and Total Dissolved Solid (TDS). During the phase of preprocessing K-Nearest Neighbors (KNN) and Random Forest were employed to handle missing data and detecting outliers. KNN imputation was applied to address missing values, while Random Forest was implemented to eliminate outliers, so generating the dataset appropriate for model training. The effectiveness of each machine learning model was assessed employing four principal metrics: accuracy, precision, recall, and F1 score. The findings revealed that all five methodologies excelled in predicting WQI; however, the XGBoost model surpassed the others, attaining the highest values across all metrics, including an accuracy of 91%.
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    An Alternative Method for Upgrading the Conventional Decision Tree Algorithm
    (2024-01-01)
    Kirdponpattara, Suppakrit
    ;
    Boonjing, Veera
    ;
    Sooraksa, Pitikhate
    Decision tree algorithms are widely used for solving classification and regression problems. Their popularity can be attributed to their transparent nature, simplicity, easy interpretability, faster classification speed, and strong decision rules. However, decision tree induction algorithms face various inherent and external limitations, such as overfitting, high sensitivity to noise and outliers, and instability with minimal data variations. In this study, we introduce an innovative approach to enhance traditional decision tree algorithms [e.g., Iterative Dichotomiser 3 (ID3), C4.5, and Classification and Regression Trees (CART)] by incorporating feature selection techniques. The proposed approach aims to enhance the accuracy and efficiency of decision tree models. Experiments were conducted on a real-world dataset of a hard disk drive (HDD) manufacturing process using the proposed approach. In comparison with a baseline where all features were utilized, the study highlighted a significant improvement in accuracy, indicating that the approach holds immense potential for optimizing decision tree algorithms and improving the HDD manufacturing process.
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    Improving Machine Learning-Based Wi-Fi Fingerprint Technique with Feature Selection and Grid Search Methods
    (2024-01-01)
    Nimnaul, Tanapol
    ;
    Wongwirat, Olarn
    This paper presents the enhancement of the WiFi-based fingerprint technique for an indoor positioning system applied to a real experimental area. In typical Wi-Fi-based fingerprint techniques, classification algorithms such as k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) are used for position estimation. However, these algorithms do not perform well with high-dimensional and large datasets. They also face limitations related to overfitting and uninformative features in datasets. This paper overcomes these challenges by deploying feature selection based on filter methods. This process removes uninformative features from the datasets before feeding them to construct a training model. The grid search method is also employed to perform hyperparameter tuning, which is used to construct the outperforming models. The experimental setup involved collecting received signal strength indicators (RSSIs) from access points (APs) in the real indoor environment to create the radio map. The accuracy performance of the proposed methods was tested by employing the feature selection method on the radio map dataset and using the grid search method to find optimized hyperparameters for constructing the training models based on the k-NN, DT, and RF algorithms. The accuracy results were compared with those of non-feature selection and default hyperparameters used for the three algorithms. The computational results demonstrate that the RF algorithm outperforms the other two algorithms. Furthermore, performance is improved when using grid search and feature selection as proposed.
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    Measuring icon recognization mapping with automated decision making system
    (2019-07-01)
    Tangtisanon, Pikulkaew
    ;
    Khongtrakan, Yanaphat
    Nowadays, mobile technology has been rapidly improved in both hardware and software aspects. Thus, many applications have been built and install in a smartphone. To find an application in the smartphone, a user has to search through various icons that design based on functions of the application. The purpose of this research is to build an automatic system that helps software designers to decide if the designed-icon is a proper icon that could be recognized by the user easily or not using entropy, Canny edge detection, and decision tree. Two experiments are reported in this research. 100 icons in both Android and iPhone operation system were used in both experiments. The sample included undergraduate students and workers in Thailand (n=90) ages ranged from 18 to 57. The first experiment was made in order to find a relationship among edge, entropy and human visual processing and use it as a training and testing dataset for the proposed system. The second experiment shows that the proposed system can be used to judge for a proper icon property with 73.33% accuracy rate.
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    Comparative Study of Prediction Models for Final GPA Score: A Case Study of Rajabhat Rajanagarindra University
    (2018-09-14)
    Putpuek, Narongsak
    ;
    Rojanaprasert, Natcha
    ;
    Atchariyachanvanich, Kanokwan
    ;
    Thamrongthanyawong, Thananya
    Recently, the analysis of educational data has become important to all universities. Rajabhat Rajanagarindra University, Thailand, wanted to study and analyze the students' performance based on their personal background. Thus, this research aimed to compare prediction models for the level of the final grade point average (GPA) score of graduated students using the data from the Faculty of Education during the 2010 to 2012 academic years. Two decision tree (C4.5 and ID3) algorithms, plus Naïve Bayes and K-nearest neighbor data mining techniques were adopted to analyze the data according to the CRISP-DM process. Factors that were proposed to influence the graduation GPA include the student's gender, scholarship awarded, previous educational background, admission type, talent and province of high school. The analysis revealed that the Naïve Bayes algorithm gave the best overall accuracy of 43.18%. This could help predict the graduation GPA score of students in the future and support teachers to make educational advice for their students and to develop the student quality in the future.
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    A New Hybrid Sampling Approach for Classification of Imbalanced Datasets
    (2018-09-11)
    Hanskunatai, Anantaporn
    Nowadays it is an era of data driven. Many organizations around the world including bank, industry, commercial, and medical intend to extract knowledge from a huge of data. But in the real-word datasets, most of them occur class imbalance problems. This paper presents a new algorithm to handle an imbalanced classification. The proposed technique is a hybrid sampling approach which is the combination of a well know oversampling algorithm called SMOTE and the undersampling technique by removing the ambiguous instances from the majority class instances. The experimental results show that the new hybrid sampling method yields the better predictive performance in term of F-measure when compare with other sampling techniques. In addition, it can improve f-measure up to 59.73% and 412.26% when compare with the original dataset based on decision tree learning and naïve bayes classifiers respectively.
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    A combination of decision tree learning and clustering for data classification
    (2011-07-21)
    Kaewchinpom, Chinnapat
    ;
    Vongsuchoto, Nattakan
    ;
    Srisawat, Anantapom
    In this paper, we present a new classification algorithm which is a combination of decision tree learning and clustering called Tree Bagging and Weighted Clustering (TBWC). The TBWC algorithm was developed to enhance a classification performance of a clustering algorithm. In the experiments, five datasets were used to evaluate the predictive performance. The experimental results show that the TBWC algorithm yields the highest accuracies when compared with decision tree learning and clustering for all datasets. In addition, this algorithm can improve the predictive performance especially for multi-class datasets which can increase the accuracy up to 36.67%. Finally, it can reduce attributes up to 59.82%. © 2011 IEEE.