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Journal articles on the topic 'CHAID'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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1

Kagnicioglu, Celal Hakan, and Mune Mogol. "Implementation of Chaid Algorithm." International Journal of Research in Business and Social Science (2147-4478) 3, no. 4 (October 22, 2014): 42–51. http://dx.doi.org/10.20525/ijrbs.v3i4.116.

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Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM) tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID) algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation.
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2

Park, Sung-Jae, Chang-Wook Lee, Saro Lee, and Moung-Jin Lee. "Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea." Remote Sensing 10, no. 10 (September 25, 2018): 1545. http://dx.doi.org/10.3390/rs10101545.

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We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.
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Santi, Vera Maya, Lina Nafisah, and Qorry Meidianingsih. "Penerapan Metode SMOTE CHAID dalam Klasifikasi Tuberkulosis Relapse." Jurnal Statistika dan Aplikasinya 6, no. 1 (June 30, 2022): 26–36. http://dx.doi.org/10.21009/jsa.06103.

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DKI Jakarta Province is one of the provinces with the highest tuberculosis cases, and a person's chance of contracting tuberculosis is the greatest among other provinces. Tuberculosis can be cured with regular treatment within a certain period of time, but after recovering, some tuberculosis sufferers may relapse so that it can cause new problems. This study aims to build a classification model and determine which factors influence tuberculosis relapse using the CHAID method. SMOTE with majority undersampling is applied as a solution to deal with the problem of patient categories (relapse and non-relapse) who have an unbalanced number of observations. Based on the CHAID classification tree, the results show that the factors that influence relapse in tuberculosis patients include the type of diagnosis, age, gender, and place of residence. In addition, the application of SMOTE can improve the performance of the CHAID classification tree in classifying patients based on their categories. These results were indicated by an increase in the values of accuracy, sensitivity, and specificity to 76,153; 26,667; and 82,608 compared to the performance of CHAID without SMOTE. Based on these results, the SMOTE CHAID classification model has better performance than CHAID
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4

Juwita, Puspa, Sugiman Sugiman, and Putriaji Hendikawati. "Ketepatan Klasifikasi Metode Regresi Logistik dan Metode Chaid dengan Pembobotan Sampel." Indonesian Journal of Mathematics and Natural Sciences 44, no. 1 (April 12, 2021): 22–33. http://dx.doi.org/10.15294/ijmns.v44i1.32699.

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Tujuan penelitian ini adalah menentukan ketepatan metode regresi logistik dan CHAID dengan pembobotan sampel pada klasifikasi status angkatan kerja Kabupaten Temanggung 2015. Populasi dalam penelitian ini adalah angkatan kerja Kabupaten Temanggung 2015. Data dalam penelitian ini diperoleh dari Sakernas Kabupaten Temanggung 2015. Variabel dependen dalam penelitian ini adalah angkatan kerja, sedangkan variabel independennya adalah klasifikasi desa/kelurahan, hubungan dengan kepala rumah tangga, jenis kelamin, umur, status pernikahan, pendidikan, pelatihan kerja, dan pengalaman kerja. Dari analisis regresi logistik diperoleh persamaan, sedangkan anlalisi CHAID menghasilkan pohon klasifikasi. Persamaan dan pohon klasifikasi tersebut dapat digunakan untuk memprediksi variabel dependen. Kesalahan klasifikasi dihitung menggunakan APER (Apparent Error Rate), kemudian ketepatan klasifikasi dapat diperoleh dengan rumus 1 – APER. Ketepatan regresi logistik dan CHAID dengan pembobotan sampel secara berturut-turut adalah 96,4% dan 96,6%. Hal ini menunjukkan ketepatan metode CHAID pada klasifikasi status angkatan kerja Kabupaten Temanggung 2015 lebih tinggi dibandingkan regresi logistik.The purpose of this study is to determine the accuracy of logistic regression and CHAID with sample weighting on Temanggung regency labor status classification in 2015. The population of this study is labor of Temanggung Regency in 2015. The data of this study is obtained from Sakernas of Temanggung Regency in 2015. The dependent variable of this study is labor status, whereas the independent variables of this study are domicile region, relation with family head, gender, age, marriage status, education level, job training, and job experience. Logistic regression analysis results a mathematic equation, and CHAID method result a classification tree. Those result can predict the dependent variable. Classification error is calculated using APER (Apparent Error Rate), then the accuracy can be calculated by 1- APER. Accuracy of logistic regression and CHAID with sample weighting respectively are 96,4% and 96,6%. This show that accuracy of CHAID is greater than logistic regression.
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Fitrianto, Anwar, Wan Zuki Azman Wan Muhamad, and Budi Susetyo. "Development of direct marketing strategy for banking industry: The use of a Chi-squared Automatic Interaction Detector (CHAID) in deposit subscription classification." Journal of Socioeconomics and Development 5, no. 1 (February 25, 2022): 64. http://dx.doi.org/10.31328/jsed.v5i1.3420.

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A comparison between Chi-squared Automatic Interaction Detector (CHAID) and logistic regression analysis was performed for classification problems on bank direct marketing data. CHAID Performance Comparison and comparison with Logistic Regression (LR) performance were also conducted. Priority performance with two statistical measures was evaluated: classification accuracy and sensitivity in the presence of data containing categorical imbalances. Random over sampling (ROS) was then applied to deal with class balance problems to get better performance of CHAID analysis. Segmentation analysis was also performed using the CHAID approach to improve the performance of the analysis results. CHAID outperforms LR because of its advantages that it can be used to perform segmentation modeling. Direct marketers should pay attention to traits are Duration, Month, Contact, and Housing. To get a higher subscription, the bank must extend the call duration. Based on these results, the banking industry needs to prepare regulations related to human resources, infrastructure, costs, and government support to achieve higher subscriptions.JEL Classification A10; C10; G21
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6

Ploquin, Anne, David Olmos, Denis A. Lacombe, Roger A'Hern, Alain Duhamel, Christopher Twelves, Silvia Marsoni, et al. "Prediction of early death among patients (pts) enrolled in phase I trials: Development and validation of a new model based on platelet count and albumin level." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 2540. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.2540.

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2540 Background: Selecting pts with “sufficient life expectancy” for phase I oncology trials remains challenging. The Royal Marsden Model (RMM; Arkenau, JCO 2009) identified high-risk pts as those with ≥2 of: albumin (ALB) <35g/l; LDH >ULN; more than 2 metastatic sites. The RMM was assessed in 1845 pts treated in phase I trials by members of the European New Drug Development Network (ENDDN). The present study aimed to develop an alternative prognostic model using a different methodology and to compare its performance with the RMM. Methods: The primary endpoint was 90-day mortality rate (90-DMR). The new model was developed from the ENDDN database using CHAID, an exploratory non-parametric data analysis method evaluating the relationship between a dependent variable (90-DMR) and possible predictive variables through a decision-tree analysis. The ROC characteristics and calibration of both methods were then validated in the independent EORTC Database (n=341 pts). Results: The CHAID method identified low and high-risk groups with 90-DMR of 9.5% vs 37.5%. High-risk pts had ALB<33g/L or ALB≥33g/L but platelet count ≥400.000/mm3. Applying both models to the validation dataset; the rates of correctly-classified pts were 0.86 [CI-95% 0.82-0.90] vs. 0.67 [CI-95% 0.60-0.74], with the CHAID model and RMM respectively. The CHAID model had higher specificity (0.90 vs. 0.65), but lower sensitivity (0.36 vs. 0.93) than the RMM. Discriminative slopes were similar for the CHAID model and RMM (19.4% and 19.3%, respectively). The negative predictive value (NPV; correct identification of pts surviving 90 days) was similar for the CHAID model and RMM (0.94 [0.91-0.97] and 0.99 [0.94-1.00] respectively). Calibration, assessed by the Brier score, was slightly better with the CHAID model (0.001 and 0.098, respectively). Conclusions: In selecting pts for phase I trials arguably an important criterion is NPV; the CHAID model and RMM provided a similar high level of NPV but the CHAID model gave a better rate of correctly-classified patients. Both models improved the screening process and reduce the attrition rate in phase I trials.
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Sa'diah, Chalimatus, Tatik Widiharih, and Arief Rachman Hakim. "KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X)." Jurnal Gaussian 10, no. 2 (May 31, 2021): 159–69. http://dx.doi.org/10.14710/j.gauss.v10i2.29923.

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One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that affect bad credit such as gender, marital status, education, employment, income, expenses, home ownership status and the dependent variable is credit status (bad and current). The analysis results show that the binary logistic regression has an accuracy value of 76.38% with an APER of 23.62%, while CHAID has an accuracy value of 93.19% with an APER of 6.81%. The accuracy value of the CHAID method is greater than the binary logistic regression method, while the APER value of the CHAID method is smaller than the binary logistic regression method. So it can be concluded that the CHAID method is better than the binary logistic regression method in classifying bad credit at company X. Keywords: Credit, Classification, Binary Logistic Regression, CHAID.
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8

FAIZA, NUR, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "METODE QUEST DAN CHAID PADA KLASIFIKASI KARAKTERISTIK NASABAH KREDIT." E-Jurnal Matematika 4, no. 4 (November 24, 2015): 163. http://dx.doi.org/10.24843/mtk.2015.v04.i04.p106.

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This aim of this research is to find out the classification results and to compare the magnitude of misclassification of QUEST and CHAID methods on the classification of customer of Adira Kredit Elektronik branch Denpasar. QUEST (Quick, Unbiased, Efficient Statistical Trees) and CHAID (Chi-squared Automatic Interaction Detection) are nonparametric methods that produce tree diagram which is easy to interpret. The QUEST and CHAID classification methods conclude that: 1) QUEST method produces three groups which predict customers into the current category, whereas CHAID method produces four groups which also predict customer into the current category; 2) both methods generate the biggest classification accuracy for customers that current category which share similar characteristics; 3) both methods also have the same degree of accuracy in classifying customer data Adira Kredit Elektronik branch Denpasar.
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9

Khodijatunnuriyah, Siti, and Hasih Pratiwi. "Klasifikasi Jenis Pencabutan Layanan oleh Pelanggan Indihome Menggunakan Metode Chi-Square Automatic Interaction Detection." Indonesian Journal of Applied Statistics 2, no. 2 (December 27, 2019): 80. http://dx.doi.org/10.13057/ijas.v2i2.34526.

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<p>Market segmentation is a classic topic in marketing which is never loss its attractiveness. In addition to market segmentation, customer satisfaction is important in the field of marketing. Customer satisfaction is a person's feelings after using goods or services produced by a company. High customer satisfaction shows a company's success in producing goods or services. Statistics provides many tools for segmentation research. One of statistical tool for segmentation research which takes the dependency method as an approach is Chi-Squared Automatic Interaction Detection (CHAID) analysis. CHAID analysis would provide decision tree like diagram which provide information about degree of association from dependent variable to the independent variables and the information about segments characteristic. In this case, the CHAID analysis is used to determine the type of service revocation segmentation by Indihome customers. Based on CHAID analysis, 25 segmentations were obtained, which consisted of revocation of the downgrade category of 45314 customers and the number of revocation of the Churn Out category by 11137 customers.</p><strong>Keywords : </strong>market segmentation, customer satisfaction<strong>, </strong>CHAID, Indihome
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10

Abbas, Ansar, Muhammad Aman Ullah, and Abdul Waheed. "Body Weight Prediction of Thalli Sheep Reared in Southern Punjab Using Different Data Mining Algorithms." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, no. 2 (December 24, 2021): 29–38. http://dx.doi.org/10.53560/ppasa(58-2)603.

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This study is conducted to predict the body weight (BW) for Thalli sheep of southern Punjab from different body measurements. In the BW prediction, several body measurements viz., withers height, body length, head length, head width, ear length, ear width, neck length, neck width, heart girth, rump length, rump width, tail length, barrel depth and sacral pelvic width are used as predictors. The data mining algorithms such as Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART) and Artificial Neural Network (ANN) are used to predict the BW for a total of 85 female Thalli sheep. The data set is partitioned into training (80 %) and test (20 %) sets before the algorithms are used. The minimum number of parent (4) and child nodes (2) are set in order to ensure their predictive ability. The R2 % and RMSE values for CHAID, Exhaustive CHAID, ANN and CART algorithms are 67.38(1.003), 64.37(1.049), 61.45(1.093) and 59.02(1.125), respectively. The mostsignificant predictor is BL in the BW prediction of Thalli sheep. The heaviest BW average of 9.596 kg is obtained from the subgroup of those having BL > 25.000 inches. On behalf of the several goodness of fit criteria, we conclude that the CHAID algorithm performance is better in order to predict the BW of Thalli sheep and more suitable decision tree diagram visually. Also, the obtained CHAID results may help to determine body measurements positively associated with BW for developing better selection strategies with the scope of indirect selection criteria.
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11

Narkevich, Artem Nikolaevich. "ИСПОЛЬЗОВАНИЕ ДЕРЕВЬЕВ КЛАССИФИКАЦИИ ДЛЯ РАСПОЗНАВАНИЯ ОБЪЕКТОВ НА ЦИФРОВЫХ ИЗОБРАЖЕНИЯХ МИКРОСКОПИЧЕСКИХ ПРЕПАРАТОВ." V mire nauchnykh otkrytiy 10, no. 3 (March 15, 2018): 12. http://dx.doi.org/10.12731/wsd-2018-3-12-23.

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Цель. Построение математических моделей деревьев классификации для распознавания объектов на цифровых микроскопических изображениях мокроты, окрашенной по методу Циля-Нильсена.Материалы и методы. Использовались данные о 177 393 объектах, выделенных на цифровых изображениях микроскопических препаратов: 6 708 объектов – кислотоустойчивые микобактерии, 170 685 – иные объекты. Анализ объектов производился по 240 цветовым и морфометрическим признакам. Для классификации объектов использовались деревья классификации, построенные различными методами.Результаты. Наибольшим показателем точности обладает дерево классификации, построенное методом Исчерпывающий CHAID, но данное дерево имеет более низкий показатель чувствительности по сравнению с деревом классификации, построенным методом CHAID. При этом последнее упомянутое дерево классификации включает в себя меньшее количество параметров объектов, необходимых для классификации. Чувствительность дерева классификации построенного методом CHAID составила 94,0 [93,4; 94,6]%, специфичность – 92,1 [92,0; 92,1]%, точность – 92,2 [92,1; 92,3]%.Заключение. Построенные с использованием различных методов деревья классификации позволяют осуществлять автоматическое распознавание объектов, выделяемых на цифровых микроскопических изображениях мокроты, окрашенной по методу Циля-Нильсена. При этом наилучшими показателями, характеризующими диагностическую способность данных моделей, для решения данной задачи обладает дерево классификации, построенное методом CHAID.
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Raharjo, Mokhamad Ramdhani. "ANALISIS ALGORITMA KLASIFIKASI DAN ASOSIASI TERHADAP ATRTIBUT DATA PELAKU USAHA MIKRO KECIL DAN MENENGAH (UMKM)." Technologia: Jurnal Ilmiah 8, no. 3 (July 10, 2017): 176. http://dx.doi.org/10.31602/tji.v8i3.1747.

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UMKM merupakan pelaku bisnis yang bergerak pada berbagai bidang usaha untuk kepentingan masyarakat. Pada saat ini UMKM dianggap sebagai cara yang efektif dalam pengentasan kemiskinan. UMKM pada saat ini sebagian besar dihadapkan dalam suatu permasalahan yang membuat usaha tersebut menjadi tidak lancar atau tidak berkembang, hal ini disebabkan oleh faktor permodalan, pengembangan kemitraan, promosi, pengembangan usaha dan sumber daya manusia. Penerapan ilmu datamining untuk menganalisa atribut pelaku usaha UMKM diperlukan untuk membantu permasalahan tersebut dengan melihat hasil uji algoritma klasifikasi yaitu Decision Tree dan CHAID serta algoritma Asosiasi Tertius. Pemberian bantuan terhadap pelaku usaha UMKM dengan cara memberi pelatihan strategi pemasaran dan penggunaan sarana prasarana penjualan sesuai proporsi masing-masing pelaku usaha dengan melihat hasil analisa algoritma datamining.Hasil pengujian menunjukan bahwa perbandingan algoritma klasifikasi Decision Tree dengan CHAID akurasi tertinggi 90.49% untuk algoritma Decision Tree sedangkan 89.51% untuk algoritma CHAID. Sedangkan pengujian algoritma asosiasi menggunakan algoritma Tertius mendapatkan kombinasi asosiasi antara nilai atribut umur, jenis kelamin, dan pekerjaan. Kata kunci : Klasifikasi, Asosiasi, UMKM, Decision Tree, CHAID, Tertius
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Balasubramaniam, C. P., and Dr R. Gunasundari. "Supply Chain Enhancement Using Improved Chaid Algorithm for Classifying the Customer Groups." International Journal of Computer Applications Technology and Research 6, no. 8 (August 8, 2017): 378–83. http://dx.doi.org/10.7753/ijcatr0608.1006.

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Nyitrai, Tamás. "CHAID-alapú felülvizsgált kategorizálás a csődelőrejelzésben." Statisztikai Szemle 97, no. 7 (July 10, 2019): 656–86. http://dx.doi.org/10.20311/stat2019.7.hu0656.

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Joun, Hyo-Jae. "Analysis of Revisiting Patterns for Korea Inbound Market: A CHAID Algorithm." Journal of Tourism Sciences 43, no. 3 (May 1, 2019): 143–58. http://dx.doi.org/10.17086/jts.2019.43.3.143.158.

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Sulviana, Via, Aji Hamim Wigena, and Indahwati. "Implementasi Metode CHAID (Chi-Squared Automatic Interaction Detection) pada Segmentasi Trend Penjualan Minuman Ringan di Indonesia." Xplore: Journal of Statistics 2, no. 2 (August 31, 2018): 24–31. http://dx.doi.org/10.29244/xplore.v2i2.91.

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Saat ini beberapa outlet mengevaluasi produknya dengan melihat trend penjualan berbagai jenis minuman ringan selama periode tertentu untuk menyusun strategi pemasaran yang efektif. Metode CHAID (Chi-Squared Automatic Interaction Detection) merupakan salah satu metode statistika non-parametrik yang efisien untuk melakukan pengklasifikasian terhadap aspek-aspek apa saja yang dapat meningkatkan penjualan minuman ringan tersebut. CHAID memilih peubah-peubah yang signifikan berdasarkan uji Khi-Kuadrat antara kategori-kategori peubah penjelas dengan kategori-kategori peubah respon berskala nominal atau ordinal. Penelitian ini bertujuan untuk mengklasifikikasikan karakteristik yang menjadi penciri keragaman dan menentukan target pasar yang mampu memaksimumkan keuntungan pada trend penjualan berbagai jenis minuman ringan dengan menggunakan metode CHAID. Hasil dari CHAID berupa diagram pohon berdasarkan segmen dari peubah penjelas yang berasosiasi terhadap peubah respon menjadi informasi yang lebih mudah dimengerti. Penelitian ini menghasilkan 11 dari 20 segmen yang dijadikan sebagai acuan dalam menentukan target pasar terhadap peningkatan trend penjualan minuman ringan yang tersebar di kota-kota besar di Indonesia. Segmen yang terbentuk berasal dari 4 peubah penjelas yaitu persebaran kota, tipe outlet, sumber pembelian, dan metode pembayaran dengan akurasi segmentasi sebesar 71.4%.
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Nguyen, Viet-Nghia, Peyman Yariyan, Mahdis Amiri, An Dang Tran, Tien Dat Pham, Minh Phuong Do, Phuong Thao Thi Ngo, Viet-Ha Nhu, Nguyen Quoc Long, and Dieu Tien Bui. "A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data." Remote Sensing 12, no. 9 (April 26, 2020): 1373. http://dx.doi.org/10.3390/rs12091373.

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Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.
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Monjezi, Nasim. "The Application of the CART and CHIAD Algorithms in Sugar Beet Yield Prediction." Basrah J. Agric. Sci. 34, no. 1 (February 4, 2021): 1–13. http://dx.doi.org/10.37077/25200860.2021.34.1.01.

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Yield prediction is a very important agricultural problem. Any farmer would like to know, as soon as possible, how much yield he can expect. The problem of predicting yield production can be solved by employing data mining techniques. This study evaluated the feasibility to predict the yield at Khuzestan Province in Iran using CART and CHAID algorithms. The analyses were performed using IBM SPSS Modeler 14.2. Three cropping seasons from 125 farms were selected between 2015 and 2018. The most important attributes were selected and the average yield was classified according to a decision tree. The data was partitioned into training (70%) and testing (30%) samples. The decision tree, including nine independent variables and 29 nodes, was produced through CART method. The decision tree, including nine independent variables and 39 nodes, was produced through the CHAID method. The CART and CHAID algorithms were evaluated using linear correlation and mean absolute error (MAE). Maximum precision of model in training part relevant to CART algorithm was equal to 95%, in testing part relevant to CART algorithm was equal to 93%. According to models′ precision, the results showed that CHAID and CART models were stable and suitable for prediction of sugar beet yield.
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Rizki, Muhammad, Muhammad Isnaini Hadiyul Umam, and Muhammad Luthfi Hamzah. "Aplikasi Data Mining Dengan Metode CHAID Dalam Menentukan Status Kredit." Jurnal Sains, Teknologi dan Industri 18, no. 1 (December 24, 2020): 29. http://dx.doi.org/10.24014/sitekin.v18i1.11421.

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Seiring dengan digalakkannya Industrial 4.0, data mining menjadi topik yang hangat untuk bahas dikalangan peneliti. Perkembangan teknologi yang begitu cepat memaksa kita untuk dapat mengambil keputusan dengan cepat pula. Kredit macet menjadi salah satu resiko terbesar lembaga keuangan. Resiko kredit macet ini wajib diminimalisir dengan menganalisa faktor status nasabah berdasarkan data personalnya, sehingga dapat dilakukan klasifikasi berdasarkan hubungan antar faktor tersebut. Salah satu kunci utama memenangkan persaingan pasar yaitu dengan menentukan target pasar. Data mining menyediakan banyak alat bantu untuk klasifikasi, salah satunya dengan menggunakan metode analisis CHAID (Chi-square Automatic Interaction Detection Analysis). Diagram pohon keputusan yang dihasilan dari Analisis CHAID dapat memberikan informasi tentang derajat hubungan antara variable independent dan dependent, serta informasi tentang karakteristik masing-masing kategori. Dalam hal ini, analisis CHAID digunakan untuk menentukan klasifikasi nasabah berdasarkan status kredit nasabah sebagai variable terikat dan data pribadi nasabah sebagai variable bebas. Dengan menggunakan uji Chi-square, dari total 7 variables independent, hanya 5 variable yang signifikan dengan variable dependent. Variable-variable tersebut adalah variable independent usia, pekerjaan, pendidikan, jangka waktu dan jumlah pinjaman. Berdasarkan hasil analisis CHAID didapatkan empat kelas. Kelas nasabah dengan pekerjaan sebagai (Aparatur Sipil Negara) ASN merupakan kelas yang memiliki resiko kredit macet yang paling minimal.
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Milanović, Marina, and Milan Stamenković. "CHAID Decision Tree: Methodological Frame and Application." Economic Themes 54, no. 4 (December 1, 2016): 563–86. http://dx.doi.org/10.1515/ethemes-2016-0029.

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Abstract Technological advancement across human activities has brought about accelerated generation of huge amounts of data. Consequently, researchers are faced with the problem how to determine adequate ways of turning the available data mass into useful knowledge. Data analysis adapted to these changes when data mining was developed as an approach to data analysis from different perspectives which reveals significant hidden regularities. This paper presents conceptual characteristics of decision tree, an important data mining method which is, due to its explorative nature, exceptionally suitable for detection of data structure when analysing various problem situations. The empirical section of the paper demonstrates applicative characteristics of this method using CHAID algorithm in leadership studies: an interdependence of selected personal characteristics and the manager’s leadership style has been investigated. The aim of the paper is to develop a classification model for identification of the dominant leadership style. The study was conducted on the sample of 417 managers of privately owned small-sized enterprises in Serbia, using a specially designed questionnaire. The classification model identified the set of six statistically significant personal characteristics as predictors of dominant leadership style.
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Haughton, Dominique, and Samer Oulabi. "Direct marketing modeling with CART and CHAID." Journal of Direct Marketing 11, no. 4 (1997): 42–52. http://dx.doi.org/10.1002/(sici)1522-7138(199723)11:4<42::aid-dir7>3.0.co;2-w.

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Haughton, Dominique, and Samer Oulabi. "Direct marketing modeling with CART and CHAID." Journal of Direct Marketing 7, no. 3 (1993): 16–26. http://dx.doi.org/10.1002/dir.4000070305.

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Hastuti, Yuli, and Muhammad Muzaini. "Algoritma Chaid Pada Klasifikasi Rumah Tangga Miskin Kota Palopo." Infinity: Jurnal Matematika dan Aplikasinya 1, no. 2 (March 6, 2021): 22–30. http://dx.doi.org/10.30605/27458326-49.

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Penelitian ini bertujun untuk mengklasifikasikan status rumah tangga miskin Kota Palopo yaitu menggunakan metode CHAID. Variabel (dependen) adalah status kemiskinan sedangkan variabel bebasnya meliputi status kepala rumah tangga, pendapatan per bulan, status pekerjaan, pendidikan terakhir kepala rumah tangga, luas lantai rumah, jenis atap rumah, jenis lantai rumah, jenis dinding rumah, status kepemilikan rumah, sumber penerangan rumah, sumber air untuk minum, tempat buang air besar, dan tempat buang sampah. Hasil analisis CHAID menunjukkan bahwa dari 13 variabel yang telah diolah hanya ada 3 variabel yang berasosiasi atau memiliki keterkaitan dengan status kemiskinan yaitu pendapatan per bulan, jenis lantai rumah, dan status pekerjaan. Variabel pendapatan per bulan sebagai child awal atau kedalaman dengan p-value 0,0001, variabel jenis lantai rumah sebagai child kedua atau kedalaman dengan p-value 0,0151, dan variabel status pekerjaan sebagai child ketiga atau kedalaman diperoleh dengan p-value 0,0001, artinya variabel pendapatan per bulan, jenis lantai rumah dan status pekerjaan memiliki keterkaitan terhadap status kemiskinan karena nilai p-value lebih kecil dari nilai = 0,05. Model CHAID yang diperoleh menggambarkan bahwa rumah tangga di Kelurahan Pontap dan Ponjalae Kecamatan Wara Timur berstatus miskin jika kepala rumah tangga yang berpendapatan < 500.000 ada yang memiliki lantai rumah keramik, atau lantai rumah semen, sedangkan untuk kategori kepala rumah tangga yang berpendapatan > 500.000 ada yang status pekerjaan berusaha sendiri dan status pekerjaan buruh tidak tetap. Tingkat ketepatan model CHAID dalam mengklasifikasikan rumah tangga miskin di Kelurahan Pontap dan Ponjalae Kecamatan Wara Timur sebesar 98,5%
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Hidayati, Isti Samrotul, and I. Made Arcana. "PENERAPAN CHAID DENGAN PENDEKATAN SMOTE PADA KEMATIAN BALITA DI KAWASAN TIMUR INDONESIA TAHUN 2017." Seminar Nasional Official Statistics 2019, no. 1 (May 13, 2020): 357–67. http://dx.doi.org/10.34123/semnasoffstat.v2019i1.97.

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Metode Chi-squared Automatic Interaction Detection (CHAID) merupakan metode segmentasi berdasarkan hubungan variabel respon dan penjelas menggunakan uji chi-square, yang dalam penerapannya perlu memperhatikan keseimbangan data untuk meminimalkan kesalahan dalam klasifikasi. Salah satu pendekatan yang dapat digunakan pada data yang tidak seimbang adalah metode Synthetic Minority Over-sampling Technique (SMOTE). Dalam penelitian ini, metode CHAID dengan pendekatan SMOTE diterapkan pada Angka Kematian Balita (AKBa) di Kawasan Timur Indonesia (KTI). Tujuannya adalah untuk mengetahui variabel-variabel yang mencirikan kematian balita berdasarkan metode analisis CHAID yang diterapkan dan membandingkannya dengan pendekatan SMOTE. Hasil perbandingan menunjukkan bahwa pendekatan SMOTE lebih baik digunakan dengan nilai sensitivitas sebesar 48,3% dan nilai presisi sebesar 75,9%. Variabel yang signifikan mencirikan kematian balita di KTI adalah berat badan saat lahir, jenis kelahiran, status bekerja ibu dan kekayaan rumah tangga, dengan karakteristik utama adalah balita yang memiliki berat badan lahir rendah dan terlahir kembar.
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Alkhasawneh, Mutasem Sh, Umi Kalthum Ngah, Lea Tien Tay, Nor Ashidi Mat Isa, and Mohammad Subhi Al-Batah. "Modeling and Testing Landslide Hazard Using Decision Tree." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/929768.

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This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.
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Stupar, Lelija, Quan Yu, and Ke Sheng Wang. "Quality Inspection with Chi-Square Automatic Interaction Detector and Self-Organizing Map." Advanced Materials Research 1039 (October 2014): 538–43. http://dx.doi.org/10.4028/www.scientific.net/amr.1039.538.

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This paper describes two methods for the industrial quality inspection: Supervised classification algorithm Chi-Square Automatic Interaction Detector (CHAID) and unsupervised clustering algorithm Self-Organizing Map (SOM). The classification and clustering are modelled in IBM software SPSS. Models’ functioning is illustrated on a wheel assembly geometric features inspection. The classifying accuracies are compared for the two methods. CHAID has shown better classifying ability than SOM, while SOM can be used to improve quality of predictor values, and therefore classifiers accuracy.
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Attar, Nasrin Fathollahzadeh, Quoc Bao Pham, Sajad Fani Nowbandegani, Mohammad Rezaie-Balf, Chow Ming Fai, Ali Najah Ahmed, Saeed Pipelzadeh, et al. "Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model." Applied Sciences 10, no. 2 (January 13, 2020): 571. http://dx.doi.org/10.3390/app10020571.

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Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow.
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Strambi, Orlando, and Karin-Anne Van De Bilt. "Trip Generation Modeling Using CHAID, a Criterion-Based Segmentation Modeling Tool." Transportation Research Record: Journal of the Transportation Research Board 1645, no. 1 (January 1998): 24–31. http://dx.doi.org/10.3141/1645-04.

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Conventional trip generation models are identified, as are the difficulties of model application typical of segmentation problems: identification and categorization of explanatory variables and of the interactions among them. The use of CHAID (Chi-Squared Automatic Interaction Detection), a criterion-based segmentation modeling tool, is explored to analyze household trip generation rates. CHAID models are presented in the form of a tree, each final node representing a group of homogenous households concerning daily trip making. An application to data from an origin-destination survey for São Paulo produced interesting results, in agreement with theoretical expectations and amenable to interpretation based on the likely activity-travel patterns of each group of households generated by the technique. CHAID can be used as an exploratory technique for aiding model development or as a model by itself. The use of CHAID results as a trip generation model was verified through an evaluation of its predictive capability in a cross comparison of two subsamples and through a comparison of observed versus predicted trips at a zone level; the segmentation of households produced by the technique provided good estimates of trip rates and zone totals. The application of a modeling approach requiring a highly disaggregate projection of the population may become possible considering the advances in methods for the generation of synthetic populations. The use of these methods in conjunction with a segmentation model represents an alternative to conventional trip generation models and an opportunity to introduce new population forecasting techniques into transportation planning practice.
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ŞATA, Mehmet. "CHAID Analizi ve Lojistik Regresyon Analizi Sonuçlarının Karşılaştırılması." Dicle Üniversitesi Ziya Gökalp Eğitim Fakültesi Dergisi, no. 33 (January 1, 2018): 48–56. http://dx.doi.org/10.14582/duzgef.1876.

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Riquier, Christopher, Rachel Kennedy, and Byron Sharp. "Behaviours versus Demographics as Identifiers of CHAID Splits." Journal of Segmentation in Marketing 2, no. 1 (April 3, 1998): 111–29. http://dx.doi.org/10.1300/j142v02n01_07.

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AL-Najjar, Dania, Hazem AL-Najjar, Nadia Al-Rousan, and Hamzeh F. Assous. "Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index." Complexity 2022 (October 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/4079524.

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The air quality index (AQI) can be described using different pollutant indices. Many investigators study the effect of stock prices and air quality in the field to show if there is a relationship between changing the stock market and the concentration of various pollutants. This study aims to find a relationship between Saudi Tadawul All Share Index (TASI) and multiple pollutants, including particulate matter (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and AQI. Based on tree models, the relationship is created using linear regression and two prediction models, Chi-square Automatic Interaction Detection (CHAID), and CR-Tree. In order to achieve the target of this research, the TASI dataset relates to six pollutants using time; afterward, the new dataset is divided into three parts—test, validate, and train—after eliminating the outlier data. In order to test the performance of two prediction models, R2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O3, SO2, and AQI indices are insignificant. The test dataset showed that R2 scores are 0.995 and 0.986 for CR-Tree and CHAID, respectively, with RMSE values of 387 and 227 for CR-Tree and CHAID, respectively. The prediction models showed that the CHAID model performed better than CR-Tree because it used only three indices, namely, PM10, AQI, and O3, with year and month. The results indicated an effect between changing TASI and the three pollutants, PM10, AQI, and O3.
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Altan, Şenol, Murat Atan, and Selman Kızılkaya. "AN EXAMINATION OF FACTORS AFFECTING GENERAL HEALTH STATUS VIA CHAID ANALYSIS, METU CASE." e-Journal of New World Sciences Academy 10, no. 3 (July 13, 2015): 92–106. http://dx.doi.org/10.12739/nwsa.2015.10.3.3c0130.

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Assous, Hamzeh F. "Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models." Complexity 2022 (April 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/3374489.

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This study aims to examine the main determinants of efficiency of both conventional and Islamic Saudi banks and then choose the best fit model among machine learning prediction models (i.e., support vector machine (SVM), Chi-squared automatic interaction detector (Chaid), linear regression, and neural network (NN)). The data were collected from the annual financial reports of Saudi banks from 2014 to 2018. The Saudi banking sector consists of 11 banks, 4 of which are Islamic. In this study, the major financial ratios are subgrouped into the profitability ratios, managerial practices, asset and loans, capital adequacy ratios, and liquidity. First, regression analysis is implemented with efficiency ratio as a dependent variable and the proxies of banks’ profitability, liquidity, asset quality, management ratios, and capital adequacy ratios as independent variables. Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models (i.e., SVM, CHAID, linear regression, and a neural network) were developed to choose the best fit. The performance metrics have also been evaluated. Regression results exhibit that the efficiency of both conventional and Islamic banks is highly affected by profitability, liquidity, and managerial practices. Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. The best predictors of cost efficiency for Saudi banks are the capital ratios, namely, CAR total and CAR tier 1. Findings are theoretically and practically important to academics, investors, and policymakers. Policymakers can benefit from the novelty of this study in building an early warning system using the CHAID model to predict different financial distress scenarios.
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AKSU, Gokhan, and Cigdem REYHANLIOGLU KECEOGLU. "Comparison of Results Obtained from Logistic Regression, CHAID Analysis and Decision Tree Methods." Eurasian Journal of Educational Research 19, no. 84 (December 3, 2019): 1–20. http://dx.doi.org/10.14689/ejer.2019.84.6.

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PUTRA, DONI, IZZATI RAHMI HG, and YUDIANTRI ASDI. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI INDEKS PRESTASI KUMULATIF (IPK) LULUSAN S-1 MATEMATIKA FMIPA UNAND DENGAN MENGGUNAKAN METODE CHAID." Jurnal Matematika UNAND 9, no. 3 (July 1, 2020): 214. http://dx.doi.org/10.25077/jmu.9.3.214-221.2020.

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Prestasi belajar merupakan hasil pencapaian yang maksimal menurut kemampuan seseorang pada waktu tertentu pada sesuatu yang dipelajari, dikerjakan, dimengerti, dan diterapkan. Pengukuran akan capaian prestasi belajar seseorang dalam perguruan tinggi ditetapkan dalam bentuk Indeks Prestasi Semester (IPS) dengan skala 0 − 4 tiap semesternya yang nantinya akan dikalkulasikan menjadi Indeks Prestasi Kumulatif (IPK) di akhir masa perkuliahan. Berdasarkan hasil Data Wisuda Jurusan Matematika tahun 2016 − 2018, lulusan banyak terdapat pada selang 3 ≤ IPK < 3.5. Di samping itu, lulusan dengan IPK selang 3.5 ≤ IPK ≤ 4 masih sedikit sekali jika dibandingkan dengan lulusan yang mempunyai IPK selang 3 ≤ IPK < 3.5. Untuk mengetahui faktor-faktor yang berpengaruh terhadap Indeks Prestasi Kumulatif (IPK) Lulusan S-1 Jurusan Matematika FMIPA Unand dilakukan suatu penelitian dengan metode klasifikasi berstruktur pohon menggunakan metode CHAID (Chi-Square Automatic Interaction Detection). Objek penelitian ini adalah Lulusan S-1 Jurusan Matematika FMIPA Unand. Berdasarkan analisis CHAID yang telah dilakukan, terdapat empat faktor yang paling berpengaruh terhadap IPK lulusan yaitu perpustakaan, kesempatan untuk berinteraksi dengan dosen di luar jadwal kuliah, ketuntasan materi perkuliahan, dan kondisi umum belajar.Kata Kunci: Prestasi belajar, Selang IPK, Uji Khi-Kuadrat (χ 2 ), Metode Berstruktur Pohon, Metode CHAID
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KAYRİ, Murat, Fuat ELKONCA, Hikmet ŞEVGİN, and Görkem CEYHAN. "The investigation of secondary school students' attitudes towards science and technology using CHAID analysis." Journal of Educational Sciences Research 4, no. 1 (April 15, 2014): 301–16. http://dx.doi.org/10.12973/jesr.2014.41.15.

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Gunduz, Murat, and Hamza M. A. Lutfi. "Go/No-Go Decision Model for Owners Using Exhaustive CHAID and QUEST Decision Tree Algorithms." Sustainability 13, no. 2 (January 15, 2021): 815. http://dx.doi.org/10.3390/su13020815.

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Go/no-go execution decisions are one of the most important strategic decisions for owners during the early stages of construction projects. Restructuring the process of decision-making during these early stages may have sustainable results in the long run. The purpose of this paper is to establish proper go/no-go decision-tree models for owners. The decision-tree models were developed using Exhaustive Chi-square Automatic Interaction Detector (Exhaustive CHAID) and Quick, Unbiased, Efficient Statistical Tree (QUEST) algorithms. Twenty-three go/no-go key factors were collected through an extensive literature review. These factors were divided into four main risk categories: organizational, project/technical, legal, and financial/economic. In a questionnaire distributed among the construction professionals, the go/no-go variables were asked to be ranked according to their perceived significance. Split-sample validation was applied for testing and measuring the accuracy of the Exhaustive CHAID and QUEST models. Moreover, Spearman’s rank correlation and analysis of variance (ANOVA) tests were employed to identify the statistical features of the 100 responses received. The result of this study benchmarks the current assessment models and develops a simple and user-friendly decision model for owners. The model is expected to evaluate anticipated risk factors in the project and reduce the level of uncertainty. The Exhaustive CHAID and QUEST models are validated by a case study. This paper contributes to the current body of knowledge by identifying the factors that have the biggest effect on an owner’s decision and introducing Exhaustive CHAID and QUEST decision-tree models for go/no-go decisions for the first time, to the best of the authors’ knowledge. From the “sustainability” viewpoint, this study is significant since the decisions of the owner, based on a rigorous model, will yield sustainable and efficient projects.
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Erdem, Sabri, Esra Aslanertik, and Bengü Yardimci. "The main determinants of differences in compliance levels of disclosure items for IAS 16 in BIST." Journal of Financial Reporting and Accounting 15, no. 3 (October 2, 2017): 317–32. http://dx.doi.org/10.1108/jfra-10-2016-0076.

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Purpose This paper aims to empirically examine the main determinants of the compliance level of disclosure requirements for IAS 16, as well as factors that may explain the differences in the levels of compliance within companies. Design/methodology/approach The association between the level of compliance and various corporate characteristics was examined using Chi-square Automatic Interaction Detector (CHAID) analysis in financial disclosures for IAS 16. CHAID analysis was applied to the manufacturing companies listed in Borsa Istanbul for the years 2012 and 2013. Findings It was found that the most significant factor is the auditor reputation within different nodes such as size or free float rate. In most of the studies, correlation is used to determine the association between different factors, but this study is the first one that uses the CHAID analysis which offers an adjusted significance testing, and at the same time classification of the interaction between variables. Research limitations/implications This paper provides insights into the primary factors of disclosure compliance that help to improve the structure of disclosures and the level of compliance in preparing future financial reports. The proposed improvements will also support further developments in financial reporting regulations regarding disclosures. The key limitation in this paper is that it concentrates on a specific standard and only covers two years. However, it provides suggestions for one of the most important standards that includes various disclosures. Originality/value In addition, this paper fills a gap in the literature about the compliance level of specific standards such as IAS 16 and the usage of CHAID analysis in such studies. The results were consistent with some previous studies regarding the relationship between compliance level, auditor reputation and size and it also highlight the effect of different disclosure items on compliance level.
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Jan, Chyan-long. "Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress." Symmetry 13, no. 3 (March 9, 2021): 443. http://dx.doi.org/10.3390/sym13030443.

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Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.
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Zeng, Jie, Panayiotis C. Roussis, Ahmed Salih Mohammed, Chrysanthos Maraveas, Seyed Alireza Fatemi, Danial Jahed Armaghani, and Panagiotis G. Asteris. "Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels." Applied Sciences 11, no. 8 (April 20, 2021): 3705. http://dx.doi.org/10.3390/app11083705.

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This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.
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Fukui, Sayato, Akihiro Inui, Mizue Saita, Daiki Kobayashi, and Toshio Naito. "Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model." Journal of International Medical Research 50, no. 1 (January 2022): 030006052110656. http://dx.doi.org/10.1177/03000605211065658.

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Objective This study was performed to identify predictive factors for bacteremia among patients with pyelonephritis using a chi-square automatic interaction detector (CHAID) decision tree analysis model. Methods This retrospective cross-sectional survey was performed at Juntendo University Nerima Hospital, Tokyo, Japan and included all patients with pyelonephritis from whom blood cultures were taken. At the time of blood culture sample collection, clinical information was extracted from the patients’ medical charts, including vital signs, symptoms, laboratory data, and culture results. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using Student’s t-test or the chi-square test and the CHAID decision tree analysis model. Results In total, 198 patients (60 (30.3%) men, 138 (69.7%) women; mean age, 74.69 ± 15.27 years) were included in this study, of whom 92 (46.4%) had positive blood culture results. The CHAID decision tree analysis revealed that patients with a white blood cell count of >21,000/μL had a very high risk (89.5%) of developing bacteremia. Patients with a white blood cell count of ≤21,000/μL plus chills plus an aspartate aminotransferase concentration of >19 IU/L constituted the high-risk group (69.0%). Conclusion The present results are extremely useful for predicting the results of bacteremia among patients with pyelonephritis.
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Suhendra, Muhammad Arif, Dwi Ispriyanti, and Sudarno Sudarno. "KETEPATAN KLASIFIKASI PEMBERIAN KARTU KELUARGA SEJAHTERA DI KOTA SEMARANG MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN METODE CHAID." Jurnal Gaussian 9, no. 1 (February 28, 2020): 64–74. http://dx.doi.org/10.14710/j.gauss.v9i1.27524.

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Menurut BPS, jumlah penduduk miskin di Kota Semarang pada Maret 2018 adalah sebesar 73,65 ribu orang. Salah satu program pemerintah dalam percepatan penanggulangan kemiskinan adalah dengan mengeluarkan Kartu Keluarga Sejahtera (KKS) yang diberikan kepada masyarakat yang kurang mampu. Penelitian ini bertujuan untuk mengetahui besarnya ukuran ketepatan klasifikasi pemberian KKS di Kota Semarang. Metode klasifikasi statistik yang digunakan adalah metode Regresi Logistik Biner dan metode Chi-Squared Automatic Interaction Detection (CHAID). Pemberian KKS dipengaruhi oleh banyak faktor, diantaranya jumlah anggota keluarga, status perkawinan, jenis kelamin kepala keluarga, usia kepala keluarga, jenjang pendidikan kepala keluarga dan kepemilikan/penguasaan HP. Pada penelitian ini, data yang digunakan adalah data sekunder hasil Survey Sosial Ekonomi Nasional (SUSENAS) tahun 2018 yang diperoleh dari Badan Pusat Statistik (BPS) Provinsi Jawa Tengah. Perbandingan data training dan testing yang digunakan adalah 60:40. Hasil analisisnya menunjukkan bahwa dengan menggunakan Regresi Logistik Biner, faktor-faktor yang berpengaruh adalah jumlah anggota keluarga dan jenjang pendidikan kepala keluarga dengan ketepatan klasifikasi sebesar 88% dan kesalahan 12%, sedangkan dengan menggunakan CHAID, faktor-faktor yang berpengaruh adalah jumlah anggota keluarga, status perkawinan, usia kepala keluarga, jenjang pendidikan kepala keluarga dan kepemilikan/penguasaan HP dengan ketepatan klasifikasi sebesar 90,2% dan kesalahan 9,8%.Kata kunci: Kartu Keluarga Sejahtera, Klasifikasi, Regresi Logistik Biner, CHAID
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43

Miller, Brian, Mark Fridline, Pei-Yang Liu, and Deborah Marino. "Use of CHAID Decision Trees to Formulate Pathways for the Early Detection of Metabolic Syndrome in Young Adults." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/242717.

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Metabolic syndrome (MetS) in young adults (age 20–39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS.Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population(n=745).Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models.Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS.
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Kaya, Murat. "Üniversite Öğrencilerinin Gelecek Beklentisinin Yapısal Eşitlik Modeli ve Chaid." Gaziosmanpasa Universitesi Sosyal Bilimler Arastirmalari Dergisi 09, no. 17 (January 1, 2014): 127. http://dx.doi.org/10.19129/sbad.272.

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Afifi Indrasari, Riefyal Arshyzha Mustain, Muhammad Tanaka, Muhammad Sukriansyah, and Edy Widodo. "Klasifikasi Antusiasme Mahasiswa terhadap Perkuliahan Daring Menggunakan Metode Chaid." Jurnal Statistika dan Aplikasinya 5, no. 2 (December 31, 2021): 166–73. http://dx.doi.org/10.21009/jsa.05205.

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Pandemi Covid-19 adalah alasan diadakannya perkuliahan daring dari berbagai kampus maupun sekolah di Indonesia, begitupun dengan kampus Universitas Islam Indonesia yang terletak di Yogyakarta. Tujuan dilakukannya penelitian ini adalah mengetahui apakah media-media yang digunakan dalam perkuliahan daring dapat menunjang kebutuhan pembelajaran baik bagi pengajar maupun bagi mahasiswa atau justru menyebabkan antusiasme mahasiswa menjadi menurun terhadap perkuliahan dengan metode yang digunakan. Penelitian ini menerapkan metode CHAID yang merupakan salah satu bagian dari metode klasifikasi. Teknik pengumpulan data dilaksanakan menggunakan angket google form lalu melanjutkan ke analisis data deskriptif dan analisis CHAID. Terdapat 1 variabel dependen yaitu variabel tingkat antusiasme mahasiswa dan 8 variabel independen yang akan digunakan yaitu variabel jenis media sosial, tingkat kehadiran mahasiswa, kegiatan persiapan mengajar dosen, metode pembelajaran, lama waktu kuliah dan intensitas pemberian tugas. Pada kasus FMIPA UII, didapatkan 6 segmen rekomendasi dari 13 segmen untuk meminimumkan antusiasme biasa saja dan tidak baik dengan tingkat akurasi sebesar 61.25%.
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Escobar Mercado, Rafael Modesto. "Las aplicaciones del análisis de segmentación: el procedimiento Chaid." Empiria. Revista de metodología de ciencias sociales, no. 1 (September 8, 2002): 13. http://dx.doi.org/10.5944/empiria.1.1998.706.

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Hyun, Soomin, and Woojin Park. "Modelling postural discomfort perception using CHAID decision tree algorithm." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1749–50. http://dx.doi.org/10.1177/1071181319631035.

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Developing quantitative models that predict discomfort levels of working postures has been an important ergonomics research topic. Such modeling not only has practical applications, but also may serve as a useful research method to improve our understanding of the human postural discomfort perception process. While the existing models have focused on achieving high prediction accuracy, less attention has been given to model interpretability, which is vital for understanding a process through modeling. Research is needed to identify the model types or modeling methods that offer high interpretability as well as good prediction accuracy. The goal of this study was to evaluate the utility of the Chi-square Automatic Interaction Detector (CHAID) decision tree modeling method in developing postural discomfort prediction models. Ten individual-specific decision tree models were developed, which predicted overall upper-body discomfort from local body part discomfort ratings. The prediction models were found to achieve high prediction accuracy and interpretability. (150 words)
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Hsu, Cathy H. C., and Soo K. Kang. "CHAID-based Segmentation: International Visitors' Trip Characteristics and Perceptions." Journal of Travel Research 46, no. 2 (November 2007): 207–16. http://dx.doi.org/10.1177/0047287507299571.

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Chaturvedi, Anil, and Paul E. Green. "Book Review: Software Review: SPSS for Windows, Chaid 6.0." Journal of Marketing Research 32, no. 2 (May 1995): 245–54. http://dx.doi.org/10.1177/002224379503200215.

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KARAKAYA, Ersin. "CHAID Algoritması ile Balık Eti Tüketimini Etkileyen Faktörlerin İncelenmesi." Journal of Agricultural Faculty of Gaziosmanpasa University 35, no. 2018-2 (January 1, 2018): 85–93. http://dx.doi.org/10.13002/jafag4381.

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