Dispersion Ratio based Decision Tree Model for Classification

Roy, Smita; Mondal, Samrat; Ekbal, Asif; Desarkar, Maunendra Sankar

doi:10.1016/j.eswa.2018.08.039

Cited by 26 publications

(13 citation statements)

References 11 publications

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“…𝑝 𝑖 is estimated by |𝐶 𝑖,𝐷 |/|𝐷| to measure the probability that D to class Ci. The evolution of entropy follows the uncertainty inherent in the dataset with a value between 0 to 1 As a classification model, the decision tree is among machine learning techniques (Roy et al, 2019). Among those different classification techniques, the decision tree algorithm is a technique regarded as an efficient method (Elouedi et al, 2000).…”

Section: Decision Tree Algorithmmentioning

confidence: 99%

The Effect of the Number of E-stores Subscribers on Chinese Smartphone Brand Purchases: Evidence From a Machine Learning Model

N'da¹,

Ge²,

Fan³

et al. 2022

Preprint

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Introduction. Until now, the impact of learning variables on consumers' choices concerning Chinese product brands in the international online shopping framework remains unknown. Accordingly, this study aims to examine the effect of those learning variables on global consumers' choices of Chinese product brands. Method. A total of 44,704 transactions related to the buying process have been collected from a programming language and the Octopus Software within a Chinese International Online Shopping platform. Analysis. The 44,704 transactions have been analyzed through a Decision Tree. Results. The study points out that the number of e-retailers' subscribers reinforces the international consumers' trust online. At the same time, the pricing levels and quantity of product availability are used by global online consumers to assess the originality of Chinese product brands. Conclusions. First, this study extends the existing literature on consumer learning by going beyond the learning variables considered. Second, the study boosts consumer learning literature by elucidating the most significant learning variables guiding international online consumers' choices and purchases. The application of the results will enable brands and e-retailers to understand (1) the stages of the international online consumers' choice; (2) the buying strategies of global consumers.

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Section: Decision Tree Algorithmmentioning

confidence: 99%

The Effect of the Number of E-stores Subscribers on Chinese Smartphone Brand Purchases: Evidence From a Machine Learning Model

N'da¹,

Ge²,

Fan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The final subset of the DT algorithm development is the Gini Index (Roy et al, 2019). It is to be noted that another decision tree algorithm, CART (Classification and Regression Tree), uses the Gini method to create split points (Zimmerman et al, 2021).…”

Section: Gini Indexmentioning

confidence: 99%

The Decision Tree Aided Neuro-Fuzzy Inference Characterization of the Stochastic Hydrology of the Tana Alluvial Aquifer

Amimo¹,

Rakesh²

2022

IJPSAT

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The Tana Alluvial Aquifer is the name given to the little-understood aquifer which is active in the areas bordering the River Tana Flow course as the river weaves its way through the sedimentary plains of Balambala, Garissa, Fafi and Ijara and, finally, into the Tana Delta areas, with the common denominator being the proximity to the Lower Tana catchment, especially the riparian corridor of the River itself, and beyond. The aquifer may extend to between five to fifteen kilometers away from the river channels course way, and at times, it may be felt even 20 kilometers away. The geology of the locality is heterogeneous and comprise sediments whose soil mechanics may not be easily deciphered, since some areas close to the river have very fresh water while others are saline (Bura East in Fafi Sub County easily comes to mind here). There are areas far from the river but bearing fresh water (Mulanjo comes to mind). In some areas, sites close to the river discharge low yield figures, whereas those located farther afield discharge favorably. The water quality and discharge are therefore stochastic variables, subject to chance occurrence. In view of this inconsistency, and on the account of data scarcity, the neuro-fuzzy inference algorithm was developed to map the Universe of Discourse of the Tana Alluvial Aquifer, aka the T.A.A., as it relates to the longitudes, latitudes, depths, and discharges of the aquifers in the study area. The mapping was with respect to aquifer discharge, the variable used to characterize an aquifer, in terms of Transmissivity and Hydraulic Conductivity, thereby defining aquifer recharge propensity. Membership functions were developed using the trapezoidal membership family, and fuzzy rules were appropriately evolved from the fuzzified aquifer data, before finally employing the Sugeno inference engines (in Python) to make predictions of discharge, at each of the T.A.A. aquifer subsets mapped for fresh, saline, hard and brackish water species. The accuracy in the outputs achieved in the areas mapped vindicated the power of the neuro-fuzzy inference systems, as the accuracy oscillated between 92 and 99 percent, when the discharge values predicted were compared with the actual known discharge values of the wells mapped. The water quality class characterization was then undertaken using the decision tree (DT) algorithm in python which gave rise to a 100 percent prediction accuracy. The same DT algorithm could not successfully predict the discrete values of aquifer discharge or EC values, with as much accuracy (but performed excellently with salinity class data), and that was why fuzzy logic was employed. The study vindicated the use of the DT and Fuzzy Logic Algorithms as simple, yet powerful analytical tools, in characterizing the Stochastic Hydrology of the Tana Alluvial Aquifer.

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“…Information application is to use the learned information to solve practical problems, such as completing informatization teaching design and making multimedia courseware. The number of times each student submitted R correctly (Roy et al, 2019).…”

Section: Online Learning Mechanismmentioning

confidence: 99%

Online Learning Behavior Feature Mining Method Based on Decision Tree

Shao¹,

Gao²,

Wang³

2022

Journal of Cases on Information Technology

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This research mainly discusses the design of online learning behavior feature mining method based on decision tree. Data collection is the real-time collection of online learning behavior data from distance learning websites. OWC (Office Web Component) technology is used to draw real-time charts on the page. Online learning students are selected as the research object, and the student's system log data and questionnaire data are selected. When combining the pre-pruning method and the post-pruning method to make decisions after the tree is pruned, the same source data is used to adjust, test and evaluate the decision tree model. The evaluation process to generate a complete decision tree is completed by the c4.5tree algorithm in C4.5, which can be named with a suffix of .names The type definition file is used to record the type of each attribute item or the range of possible values. In the study, the prediction accuracy rate of predicting learning effect based on "online learning behavior" reached more than 66%.

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Dispersion Ratio based Decision Tree Model for Classification

Cited by 26 publications

References 11 publications

The Effect of the Number of E-stores Subscribers on Chinese Smartphone Brand Purchases: Evidence From a Machine Learning Model

The Effect of the Number of E-stores Subscribers on Chinese Smartphone Brand Purchases: Evidence From a Machine Learning Model

The Decision Tree Aided Neuro-Fuzzy Inference Characterization of the Stochastic Hydrology of the Tana Alluvial Aquifer

Online Learning Behavior Feature Mining Method Based on Decision Tree

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