Meta Decision Trees for Explainable Recommendation Systems

Shulman, Eyal; Wolf, Lior

doi:10.1145/3375627.3375876

Cited by 11 publications

(6 citation statements)

References 19 publications

(15 reference statements)

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“…Different authors have adopted different algorithms for making recommendations, which are summarized below: KNNs have been used as a meta-learner for the aim of algorithm selection in many studies such as Kalousis and Theoharis (1999), Song et al (2012), Wang et al (2013), Tripathy and Panda (2017), Abdulrahman et al (2018) and Zhang et al (2019). Decision Trees is used as a meta-learner in many studies such as Brazdil et al (1994, April), Ali and Smith (2006) and Shulman and Wolf (2020). Regression methods have also been used as meta-learner in Leyva et al (2014), Reif et al (2014), Bensusan and Kalousis (2001) and Garcia et al (2016). …”

Section: Related Workmentioning

confidence: 99%

“…Decision Trees is used as a meta-learner in many studies such as Brazdil et al (1994, April), Ali and Smith (2006) and Shulman and Wolf (2020).…”

Section: Related Workmentioning

confidence: 99%

“…,Song et al (2012),Wang et al (2013),Tripathy and Panda (2017),Abdulrahman et al (2018) andZhang et al (2019). Decision Trees is used as a meta-learner in many studies such asBrazdil et al (2008),Ali and Smith (2006) andShulman and Wolf (2020). Regression methods have also been used as meta-learner inLeyva et al (2014),Reif et al (2014),Bensusan and Kalousis (2001) andGarcia et al (2016).…”

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confidence: 99%

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Cost-sensitive meta-learning framework

Shilbayeh

Vadera

2021

JM2

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Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

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Section: Related Workmentioning

confidence: 99%

“…Decision Trees is used as a meta-learner in many studies such as Brazdil et al (1994, April), Ali and Smith (2006) and Shulman and Wolf (2020).…”

Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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Cost-sensitive meta-learning framework

Shilbayeh

Vadera

2021

JM2

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“…Other work used GP to generate a set of comprehensible decision rules to identify cases of bankruptcy [10]. Decision-trees have been used for classification in a wide range of applications, with recent proposals for developing meta decision trees for explainable recommendation systems [14]. Based on this, we select GP and decision trees as appropriate candidates to generate rules.…”

Section: Previous Workmentioning

confidence: 99%

Automated, Explainable Rule Extraction from MAP-Elites Archives

Urquhart

Höhl

Hart

2021

Applications of Evolutionary Computation

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Quality-diversity (QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user -they are often described as illuminating the feature-space, providing a qualitative illustration of relationships between features and objective quality. However, if there are 1000s of solutions in an archive, extracting a succinct set of rules that capture these relationships in a quantitative manner (i.e. as a set of rules) is challenging. We propose two methods for the automated generation of rules from data contained in an archive; the first uses Genetic Programming and the second, a rule-induction method known as CN2. Rules are generated from large archives of data produced by running MAP-Elites on an urban logistics problem. A quantitative and qualitative evaluation that includes the end-user demonstrate that the rules are capable of fitting the data, but also highlights some mismatches between the model used by the optimiser and that assumed by the user.

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“…Traditional machine learning algorithms like linear regression [17], logistic regression [18], support vector machines [19] and decision tree models [20] try to model the data in a linear fashion. But many times, data in real-world is non-linear.…”

Section: Deep Learning In Recommender Systemmentioning

confidence: 99%

Data Science and Deep Learning Applications in the E-Commerce Industry: A Survey

Pradeep¹,

M²,

Satyanarayana³

2020

INDJCSE

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The E-Commerce industry is a new way of shopping. Most manufacturers prefer to sell their products on E-commerce websites because of low maintenance and high returns. This paper highlights the application of data science and deep learning methodologies to drive the E-commerce business. The first part of the paper explains the role of data science to identify potential customers, items recommendations and fraud detections. Different stages of building E-commerce websites and the role of proper handling of data is then highlighted. The second part of the paper is about the use of deep learning approaches like Recurrent neural networks in analysing textual information is presented with a demonstration of three architectures. At last, the importance of convolutional neural networks in the E-commerce industry is presented. Fashion related images like dresses, shoes and bags are classified using two separate convolution neural network architectures.

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Meta Decision Trees for Explainable Recommendation Systems

Cited by 11 publications

References 19 publications

Cost-sensitive meta-learning framework

Cost-sensitive meta-learning framework

Automated, Explainable Rule Extraction from MAP-Elites Archives

Data Science and Deep Learning Applications in the E-Commerce Industry: A Survey

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