Feature selection for text classification using genetic algorithms

Bidi, Noria; Elberrichi, Zakaria

doi:10.1109/icmic.2016.7804223

Cited by 46 publications

(23 citation statements)

References 6 publications

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“…Batujajar diperlukan metode pemilihan fitur. Pemilihan atribut/fitur merupakan tugas penting dalam berbagai domain data mining yang merupakan salah satu teknik yang paling sering dan penting dalam pra-pengolahan data, dan telah menjadi komponen yang sangat diperlukan dalam proses pembelajaran mesin [8]. Penulis telah mempertimbangkan berbagai teknik pemilihan fitur untuk berbagai aplikasi di berbagai domain.…”

Section: Pendahuluanunclassified

Algoritma Genetika untuk Optimalisasi Klasifikasi Kepuasan Pelayanan e-KTP

Sugianto¹,

Apandi²

2019

Preprint

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Pelayanan publik adalah segala kegiatan pelayanan yang dilaksanakan oleh penyelenggara pelayanan publik sebagai upaya pemenuhan kebutuhan penerima pelayanan, maupun dalam rangka pelaksanaan ketentuan peraturan perundang -undangan. Salah satu pelayanan public yang paling sering dikeluhan adalah E-KTP menurut data sisa perekaman masih banyak yang belum melakukan perekaman data tersebut. Untuk melihat kepuasan mayarakat terhadap pelayanan public perlu dibuatnya klasifikasi keluhanan masyarakat. Keluhan dari masyarakat dikumpulkan dalam bentuk pertanyaan yang telah disebar sebelumnya. Data yang dikumpulkan akan diklasifikasi menggunakan metode Naïve Bayes ditambah dengan optimasi menggunakan Algoritma Genetik. Hasil klasifikasi akan didapat nilai akurasinya. Akurasi akan dibandingkan pengaruh optimasinya. Pengujian akurasi menunjukan Naïve Bayes ditambah optimasi menggunakan Algoritma Genetik dengan pemilihan Roulette Wheel memiliki nilai akurasi yang lebih baik dengan nilai akurasi 85,64% dibandingkan dengan pemilihan skema default dengan nilai akurasi 83.03.

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Section: Pendahuluanunclassified

Algoritma Genetika untuk Optimalisasi Klasifikasi Kepuasan Pelayanan e-KTP

Sugianto¹,

Apandi²

2019

Preprint

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“…The use of efficient machine learning algorithms is an important task in features selection as reduction dimensions. However, the proliferation of this feature selection technique raises the difficulty of choosing the algorithm for selecting the features that are most suitable for an application, resulting from the selection of different features [60].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Software Effort Estimation Models Based On Metaheuristic Methods: A Proposed Framework

Marco¹

2019

IJATCSE

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Software effort estimation are part of the field of project management in software that is very important for development efforts. Software development planning is something very complex and serious, which determines the success of a software project. Because of the lack of good requirements and information, it causes software project failures. Although there are many studies that aim to solve the problem of noisy, irrelevant and excessive data to achieve accuracy. The purpose of this study is to combine metaheuristic optimization techniques as a framework for using Machine Learning models. By proposing a hybrid estimation model based on a combination of the Satin Bowerbird Optimizer (SBO) algorithm and Support Vector Regression (SVR) to improve the accuracy of software estimation efforts. This study is to determine the effort estimation and duration estimation. The proposed framework is based on theoretical concepts. the proposed model will be tested using a heterogeneous dataset, namely the ISBSG dataset. the results of the study are expected to be used as decision making as the initial planning of software project development.

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“…classification accuracy and hamming gap .The limitation of the approach is normalization concept is not used. Bidi et al [23] reviewed a feature selection methods based on genetic algorithms for different text representation methods. This states the empirical study of the component determination for the genetic algorithms for the different methods of text representation.…”

Section: Literature Surveymentioning

confidence: 99%

An efficient technique for hybrid classification and feature extraction using normalization

Kaur¹,

Bathla²

2018

IJET

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Text classification is technique for assigning the class or label to a particular document within predefined class labels. Predefined classes examples are sports, business, technical, education and science etc. Classification is supervised learning technique i.e. these classes are trained with certain features and then document is classified based on similarity measure with these trained document set. Text classification is used in many applications like assigning the label to the documents, separating the spam messages from the genuine one, filtering of text, natural language processing etc. Feature selection, extraction and classification are various phases for assigning label to any document. In this paper, PCA is used for feature extraction, ABC is used for feature selection and SVM is used for classification. PCA is improved by applying normalization-using size of features in our proposed approach. It reduces the redundant features to larger extent. There are very few research works, which have implemented PCA, ABC and SVM for complete classification. Evaluation parameters like accuracy, F-measure and G-mean are calculated to check classifier efficiency. The proposed system is deployed on 20-Newsgroup dataset. Experiment analysis proves that accuracy is improved using our proposed approach as compared to existing approaches.

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Feature selection for text classification using genetic algorithms

Cited by 46 publications

References 6 publications

Algoritma Genetika untuk Optimalisasi Klasifikasi Kepuasan Pelayanan e-KTP

Algoritma Genetika untuk Optimalisasi Klasifikasi Kepuasan Pelayanan e-KTP

Optimizing Software Effort Estimation Models Based On Metaheuristic Methods: A Proposed Framework

An efficient technique for hybrid classification and feature extraction using normalization

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