Implications of deep learning for the automation of design patterns organization
Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the...
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| Vydané v: | Journal of parallel and distributed computing Ročník 117; s. 256 - 266 |
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| Hlavní autori: | , , , , , , , |
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| Jazyk: | English |
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Elsevier Inc
01.07.2018
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| ISSN: | 0743-7315, 1096-0848 |
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| Abstract | Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier’s performance in terms of organization of design patterns.
•There is a need to bridge the gap between the semantic relationship between patterns.•We propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN).•The DBN learns on the semantic representation of documents formulated in the form of feature vectors.•We performed a case study in the context of a text categorization based automated system.•The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set. |
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| AbstractList | Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier’s performance in terms of organization of design patterns.
•There is a need to bridge the gap between the semantic relationship between patterns.•We propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN).•The DBN learns on the semantic representation of documents formulated in the form of feature vectors.•We performed a case study in the context of a text categorization based automated system.•The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set. |
| Author | Piccialli, Francesco Keung, Jacky Akhunzada, Adnan Khan, Arif Ali Jeon, Gwanggil Hussain, Shahid Cuomo, Salvatore Ahmad, Awais |
| Author_xml | – sequence: 1 givenname: Shahid surname: Hussain fullname: Hussain, Shahid email: Shussain7-c@my.cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong – sequence: 2 givenname: Jacky surname: Keung fullname: Keung, Jacky email: jacky.keung@cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong – sequence: 3 givenname: Arif Ali surname: Khan fullname: Khan, Arif Ali email: aliakhan2-c@my.cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong – sequence: 4 givenname: Awais surname: Ahmad fullname: Ahmad, Awais email: aahmad.marwat@gmail.com organization: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea – sequence: 5 givenname: Salvatore surname: Cuomo fullname: Cuomo, Salvatore email: salvatore.cuomo@unina.it organization: University of Naples Federico II, Naples, Italy – sequence: 6 givenname: Francesco surname: Piccialli fullname: Piccialli, Francesco email: francesco.piccialli@unina.it organization: University of Naples Federico II, Naples, Italy – sequence: 7 givenname: Gwanggil surname: Jeon fullname: Jeon, Gwanggil email: gjeon@inu.ac.kr organization: Department of Embedded Systems Engineering, Incheon National University, Republic of Korea – sequence: 8 givenname: Adnan surname: Akhunzada fullname: Akhunzada, Adnan email: a.qureshi@comsats.edu.pk organization: Comsats Institute of Information Technology, Islamabad, Pakistan |
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