Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems—A Participatory Design Approach

Razak, Tajul Rosli; Garibaldi, Jonathan M.; Wagner, Christian; Pourabdollah, Amir; Soria, Daniele

doi:10.1109/tfuzz.2020.2969901

Cited by 41 publications

(14 citation statements)

References 51 publications

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“…To derive the criteria for the recommender system, i.e., similarity measures, we suggest a participatory architecture approach. As previously said, participatory design is a method of including consumers in the design creation process in order to ensure that the end product meets their requirements [7], [20]. A participatory design process is described in this section, and it consists of two main steps: i) measuring the recommender system using multiple similarity measures; and ii) matching the recommender system (similarity measures) with the participatory users.…”

Section: Experiments and Results-participatory Design Approachmentioning

confidence: 99%

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A framework to shape the recommender system features based on participatory design and artificial intelligence approaches

Razak

Ismail

Fauzi

et al. 2021

IJ-AI

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<span lang="EN-GB">A recommender system is an algorithm aiming at giving suggestions to users on relevant elements or items such as products to purchase, books to read, jobs to apply or anything else depending on industries or situations. Recently, there has been a surge in interest in developing a recommender system in a variety of areas. One of the most widely used approaches in recommender systems is collaborative filtering (CF). The CF is a strategy for automatically creating a filter based on a user's needs by extracting desires or recommendation information from a large number of users. The CF approach uses multiple correlation steps to do this. However, the occurrence of uncertainty in finding the best similarity measure is unavoidable. This paper outlines a method for improving the configuration of a recommender system that is tasked with recommending an appropriate study field and supervisor to a group of final-year project students. The framework we suggest is built on a participatory design methodology that allows students' individual opinions to be factored into the recommender system's design. The architecture of the recommender scheme was also illustrated using a real-world scenario, namely mapping the students' field of interest to a possible supervisor for the final year project.</span>

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Section: Experiments and Results-participatory Design Approachmentioning

confidence: 99%

“…Razak et al [19], [20] discovered that people are far more comfortable offering rankings than numerical values in their research. As a result, for their final year project, we also asked students to rate the questions in their area of interest.…”

Section: Participatory Student Uitm Perlismentioning

confidence: 99%

A framework to shape the recommender system features based on participatory design and artificial intelligence approaches

Razak

Ismail

Fauzi

et al. 2021

IJ-AI

Self Cite

View full text Add to dashboard Cite

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“…Hence, we have proposed the way of producing this study, as explained in the following subsection. Note that this approach has aspired from the studies conducted by [14] and [15].…”

Section: A User Study Approachmentioning

confidence: 99%

Assessing Student Programming Skills and Area of Interests in the Final Year Project

Razak¹,

Ismail²,

Fauzi³

et al. 2021

IJCDS

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Final Year Project (FYP) is an essential ingredient of an undergraduate programme as it creates a platform for students to strengthen their problem-solving and research skills. However, in the case of Information Technology (IT) programme, students are lack of knowledge in choosing the most suitable area of interest that complements their programming skills. Also, the issues arise, such as: "How to select the most suitable area of interest for the final project?" and "Do programming skills offer benefits in choosing the area of interest for the final project?". This paper proposes a method to assess the meaningful relationship between a student programming skill and the area of interest for the final year project with the aided of a user study by capturing stu dents' perception on these two qualities in selecting their final year project. The obtained correlation between the two qualities, which is the key contribution of the paper, creates a platform for further research into developing a recommendation system that can assis t final year projects students in selecting the most suitable research area for their FYP.

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“…In [31], Razak et al introduced the H (for HFS interpretability) framework, which incorporates aggregate interpretability at each subsystem into a single overall metric of HFS interpretability. It can be described in the following way:…”

Section: Interpretabilitymentioning

confidence: 99%

Hierarchical Fuzzy Systems: Interpretability and Complexity

Razak

Fauzi

Gining

et al. 2021

IJEEI

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Rule explosion related to an increase in the number of input variables has been recognised as a key issue in fuzzy logic systems (FLSs), and hierarchical fuzzy systems (HFSs) have been proposed as a viable solution. The typical FLS subsystem system is transformed into a low-dimensional FLS subsystem network in HFS. Furthermore, because the number of input variables in each subsystem is reduced, the rules in HFS generally contain antecedents with fewer variables than the rules in regular FLS with similar functions. As a result, HFSs can reduce rule explosion, reducing model complexity and improving model interpretability. Nonetheless, the concerns concerning the issue of "Does reducing the complexity of HFSs with various subsystems, layers, and diverse topologies actually increase their interpretability?" remain unclear. In this study, we compare two HFS topologies: parallel and serial, concentrating on interpretability and complexity. For both topologies, a full measurement of the interpretability and complexity with various configurations is presented. The goal of this comparative study is to see if there is a correlation between HFS interpretability and complexity.

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Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems—A Participatory Design Approach

Cited by 41 publications

References 51 publications

A framework to shape the recommender system features based on participatory design and artificial intelligence approaches

A framework to shape the recommender system features based on participatory design and artificial intelligence approaches

Assessing Student Programming Skills and Area of Interests in the Final Year Project

Hierarchical Fuzzy Systems: Interpretability and Complexity

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