An Exploratory Technique for Investigating Large Quantities of Categorical Data

By G. V. Kass. 1980

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Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites (2020, methodology): “1984), Chi-squared automatic interaction detection (CHAID) (Kass 1980), C5 (Quinlan 1993), and quick, unbiased, efficient, and statistical tree (QUEST) (Loh and Shih 1997).”
Decision tree methods: applications for classification and prediction (2015, methodology): “building decision tree models Several statistical algorithms for building decision trees are available, including CART (Classification and Regression Trees), [7] C4.5, [8] CHAID (Chi-Squared Automatic Interaction Detection), [9] and QUEST (Quick, Unbiased, Efficient, Statistical Tree).”
Is It Who You Know, or How Many That Counts? Criminal Networks and Cost Avoidance in a Sample of Young Offenders (2010, methodology): “The CHAID algorithm starts by examining the statistical significance between each predictor and the dependent variable, until the most statistically significant chi-square value is found (Kass, 1980).”
Indicators for the representativeness of survey response (2009, methodology): “Methods that support such estimation are, for instance, logistic or probit regression models (Agresti 2002) and CHAID classification trees (Kass 1980).”
Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry (2008, methodology): “In particular, several algorithms such as CART (Breiman, Friedman, Olshen, & Stone, 1984), CHAID (Kass, 1980), ID3 (Quinlan, 1986), and C4.”
Classification and regression trees (2011, unknown): “CTree, 14 CHAID 15 employs yet another strategy.”
Identifying and Selecting the Common Elements of Evidence Based Interventions: A Distillation and Matching Model (2005, background): “Such algorithms typically use a selected test statistic to identify points of similarity and difference among cells in the problem space, building a decision tree that outlines important interactions (e.g., Chi Square Automated Interaction Detector; Kass, 1980).”
A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending (2011, methodology): “Subjects not classified into designated groups (either high risk or low risk) in the first iteration of CHAID were pooled together and re-analyzed in a second iteration of CHAID.”
A personalized recommender system based on web usage mining and decision tree induction (2002, methodology): “There are well-known decision tree induction algorithms such as CHAID (Kass, 1980), CART (Beiman, Friedman, Olshen, & Stone, 1984), C4.5 (Quinlan, 1993) and QUEST (Loh & Shih, 1997), etc. Applications of decision tree based classification include target marketing, churn prediction, medical…”
The Impact of Customer Satisfaction on Share-of-Wallet in a Business-to-Business Environment (2003, methodology): “An initial exploratory analysis was conducted using chi-square automatic interaction detection (CHAID) (Babinec 1990; Kass 1980; Magidson 1993; Perreault and Barksdale 1980).1 Overall satisfaction levels were used as a predictor of share-of-wallet (SOW).2 The results indicated that the optimal…”
Early Childhood Longitudinal Study, Birth Cohort (ECLS-B): Methodology Report for the 9-Month Data Collection (2001-02). Volume 2: Sampling. NCES 2005-147. (2005, methodology): “These characteristics were selected based on analyses using segmentation modeling via the Chi-Squared Automatic Interaction Detector (CHAID) (see FloresCervantes et al. 2002; Kass 1980; Rizzo, Kalton, and Brick 1996).”
Modeling and forecasting building energy consumption: A review of data-driven techniques (2019, background): “The most common for building energy consumption forecasting are classification and regression trees (CART) (Breiman, Friedman, Olshen, & Stone, 1984), chisquared automatic interaction detector (CHAID) (Kass, 1980), ID3 (J R Quinlan, 1986), C4.5 (John Ross Quinlan, 1993), and C5.”