Features Selection for Classification of SMILES Codes Based on Their Function

Dian Eka Ratnawati; Syaiful Anam

doi:10.1109/ISRITI48646.2019.9034619

Features Selection for Classification of SMILES Codes Based on Their Function

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The classification of the active compound based on their function is important to be done because most of them are unknown their function. The structure of the active compound can be represented by SMILES code that unique, compact and complete. Preprocessing SMILES code is a crucial task before SMILES codes are classified. In this research, preprocessing is extracting SMILES code into several features. The features must represent patterns or information from SMILES codes because the proper features of the SMILES codes will increase the accuracy of classification results. This paper uses features from SMILES codes directed by an expert and based on the previous research. Features will be normalized and are classified by an efficient and good classification method, Extreme Learning Machine (ELM). The experiment results show that first, adding features will increase the average of the accuracy of the system until 10.9% on dataset 1-3-4 (nerve-bacterial-cancer). Second, ELM is superior to SVM and KMNB in terms of both accuracy and processing time.

Original language	English
Title of host publication	2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019
Editors	Ferry Wahyu Wibowo
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	103-108
Number of pages	6
ISBN (Electronic)	9781728145204
DOIs	https://doi.org/10.1109/ISRITI48646.2019.9034619
Publication status	Published - Dec 2019
Event	2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019 - Yogyakarta, Indonesia Duration: 5 Dec 2019 → 6 Dec 2019

Publication series

Name	2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019

Conference

Conference	2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019
Country/Territory	Indonesia
City	Yogyakarta
Period	5/12/19 → 6/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

classification
Extreme Learning Machine
feature selection
SMILES

Access to Document

10.1109/ISRITI48646.2019.9034619

Cite this

Ratnawati, D. E., Marjono , Widodo , & Anam, S. (2019). Features Selection for Classification of SMILES Codes Based on Their Function. In F. W. Wibowo (Ed.), 2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019 (pp. 103-108). Article 9034619 (2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISRITI48646.2019.9034619

Ratnawati, Dian Eka ; Marjono, ; Widodo et al. / Features Selection for Classification of SMILES Codes Based on Their Function. 2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019. editor / Ferry Wahyu Wibowo. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 103-108 (2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019).

@inproceedings{cf46162b599f453ca6abb8cd33b18868,

title = "Features Selection for Classification of SMILES Codes Based on Their Function",

abstract = "The classification of the active compound based on their function is important to be done because most of them are unknown their function. The structure of the active compound can be represented by SMILES code that unique, compact and complete. Preprocessing SMILES code is a crucial task before SMILES codes are classified. In this research, preprocessing is extracting SMILES code into several features. The features must represent patterns or information from SMILES codes because the proper features of the SMILES codes will increase the accuracy of classification results. This paper uses features from SMILES codes directed by an expert and based on the previous research. Features will be normalized and are classified by an efficient and good classification method, Extreme Learning Machine (ELM). The experiment results show that first, adding features will increase the average of the accuracy of the system until 10.9\% on dataset 1-3-4 (nerve-bacterial-cancer). Second, ELM is superior to SVM and KMNB in terms of both accuracy and processing time.",

keywords = "classification, Extreme Learning Machine, feature selection, SMILES",

author = "Ratnawati, \{Dian Eka\} and Marjono and Widodo and Syaiful Anam",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019 ; Conference date: 05-12-2019 Through 06-12-2019",

year = "2019",

month = dec,

doi = "10.1109/ISRITI48646.2019.9034619",

language = "English",

series = "2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "103--108",

editor = "Wibowo, \{Ferry Wahyu\}",

booktitle = "2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019",

}

Ratnawati, DE , Marjono, , Widodo, & Anam, S 2019, Features Selection for Classification of SMILES Codes Based on Their Function. in FW Wibowo (ed.), 2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019., 9034619, 2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019, Institute of Electrical and Electronics Engineers Inc., pp. 103-108, 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019, Yogyakarta, Indonesia, 5/12/19. https://doi.org/10.1109/ISRITI48646.2019.9034619

Features Selection for Classification of SMILES Codes Based on Their Function. / Ratnawati, Dian Eka ; Marjono, ; Widodo et al.
2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019. ed. / Ferry Wahyu Wibowo. Institute of Electrical and Electronics Engineers Inc., 2019. p. 103-108 9034619 (2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Features Selection for Classification of SMILES Codes Based on Their Function

AU - Ratnawati, Dian Eka

AU - Marjono,

AU - Widodo,

AU - Anam, Syaiful

PY - 2019/12

Y1 - 2019/12

N2 - The classification of the active compound based on their function is important to be done because most of them are unknown their function. The structure of the active compound can be represented by SMILES code that unique, compact and complete. Preprocessing SMILES code is a crucial task before SMILES codes are classified. In this research, preprocessing is extracting SMILES code into several features. The features must represent patterns or information from SMILES codes because the proper features of the SMILES codes will increase the accuracy of classification results. This paper uses features from SMILES codes directed by an expert and based on the previous research. Features will be normalized and are classified by an efficient and good classification method, Extreme Learning Machine (ELM). The experiment results show that first, adding features will increase the average of the accuracy of the system until 10.9% on dataset 1-3-4 (nerve-bacterial-cancer). Second, ELM is superior to SVM and KMNB in terms of both accuracy and processing time.

AB - The classification of the active compound based on their function is important to be done because most of them are unknown their function. The structure of the active compound can be represented by SMILES code that unique, compact and complete. Preprocessing SMILES code is a crucial task before SMILES codes are classified. In this research, preprocessing is extracting SMILES code into several features. The features must represent patterns or information from SMILES codes because the proper features of the SMILES codes will increase the accuracy of classification results. This paper uses features from SMILES codes directed by an expert and based on the previous research. Features will be normalized and are classified by an efficient and good classification method, Extreme Learning Machine (ELM). The experiment results show that first, adding features will increase the average of the accuracy of the system until 10.9% on dataset 1-3-4 (nerve-bacterial-cancer). Second, ELM is superior to SVM and KMNB in terms of both accuracy and processing time.

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KW - Extreme Learning Machine

KW - feature selection

KW - SMILES

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DO - 10.1109/ISRITI48646.2019.9034619

M3 - Conference contribution

AN - SCOPUS:85083326842

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BT - 2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019

Y2 - 5 December 2019 through 6 December 2019

ER -

Ratnawati DE , Marjono , Widodo , Anam S. Features Selection for Classification of SMILES Codes Based on Their Function. In Wibowo FW, editor, 2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 103-108. 9034619. (2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019). doi: 10.1109/ISRITI48646.2019.9034619

Features Selection for Classification of SMILES Codes Based on Their Function

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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