A Model of E-Commerce Self-Assessment System Based on E-Customer Behavior

Zaim, Houda; Ramdani, Mohammed; Haddi, Adil

doi:10.2139/ssrn.3179244

Cited by 3 publications

(2 citation statements)

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“…Before delving into specific approaches. One possible approach involves using a similarity formula to identify the attribute that exhibits the highest degree of similarity, which then becomes the splitting node (Zaim, Ramdani, & Haddi, 2018). Another method utilizes a Penalized Linear Discriminant Analysis (PLDA), which is based on clusters of variables, to construct a classification rule (Poterie, Dupuy, Monbet, & Rouviere, 2019).…”

Section: Importance Of Classification Tree Algorithm In Machine Learningmentioning

confidence: 99%

Unraveling the Significance of the Classification Tree Algorithm in Machine Learning: A Literature Review

Bensi,

Esquivel

2023

ejtas

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Machine learning, an integral component of Artificial Intelligence (AI), empowers systems to autonomously enhance their performance through experiential learning. This paper presents a comprehensive overview of the Classification Tree Algorithm's pivotal role in the realm of machine learning. This algorithm simplifies the process of categorizing new instances into predefined classes, leveraging their unique attributes. It has firmly established itself as a cornerstone within the broader landscape of classification techniques. This paper delves into the multifaceted concepts, terminologies, principles, and ideas that orbit the Classification Tree Algorithm. It sheds light on the algorithm's essence, providing readers with a clearer and more profound understanding of its inner workings. By synthesizing a plethora of existing research, this endeavor contributes to the enrichment of the discourse surrounding classification tree algorithms. In summary, the Classification Tree Algorithm plays a fundamental role in machine learning, facilitating data classification, and empowering decision-making across domains. Its adaptability, alongside emerging variations and innovative techniques, ensures its continued relevance in the ever-evolving landscape of artificial intelligence and data analysis.

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Section: Importance Of Classification Tree Algorithm In Machine Learningmentioning

confidence: 99%

Unraveling the Significance of the Classification Tree Algorithm in Machine Learning: A Literature Review

Bensi,

Esquivel

2023

ejtas

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“…Restrictions placed on fuzzy variable's values to define the possible changes are based on e-customer behavior during navigation sessions:S = S = ∪ ∪ ∪ respectively, are visit sessions, consultation, basket session and purchase session [28]…”

Section: Fuzzificationmentioning

confidence: 99%

A novel approach to dynamic profiling of e-customers considering click stream data and online reviews

Zaim¹,

Haddi²,

Ramdani³

2019

IJECE

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<p>In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.</p><p> </p>

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Fuzzy-Based Mining Framework of Browsing Behavior to Enhance E-commerce Website Performance

Zaim

Ramdani

Haddi

2018

Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications

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A Model of E-Commerce Self-Assessment System Based on E-Customer Behavior

Cited by 3 publications

References 9 publications

Unraveling the Significance of the Classification Tree Algorithm in Machine Learning: A Literature Review

Unraveling the Significance of the Classification Tree Algorithm in Machine Learning: A Literature Review

A novel approach to dynamic profiling of e-customers considering click stream data and online reviews

Fuzzy-Based Mining Framework of Browsing Behavior to Enhance E-commerce Website Performance

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