Multiclass classification of cancer based on microarray data using extreme learning machine

Khadijah, Khadijah; Rismiyati,; Mantau, Aprinaldi Jasa

doi:10.1109/icicos.2017.8276355

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…It is a version of the Relief algorithm and developed for multi-class datasets. It gives better results in the noisy and missing data [24]. The main objective of this method is to rate each feature according to its ability to distinguish nearest samples.…”

Section: Relief-fmentioning

confidence: 99%

Gene expression data classification using genetic algorithm-based feature selection

SÖNMEZ¹,

Dağtekin²,

Ensari³

2021

Turk J Elec Eng & Comp Sci

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In this study, hybrid methods are proposed for feature selection and classification of gene expression datasets.In the proposed genetic algorithm / support vector machine (GA-SVM) and genetic algorithm / k nearest neighbor (GA-KNN) hybrid methods, genetic algorithm is improved by Pearson correlation coefficient, Relief-F or mutual information.Crossover and selection operations of the genetic algorithm are specialized. Eight different gene expression datasets are used for classification process. The classification performances of proposed methods are compared with the traditional GA-KNN and GA-SVM wrapper methods and the other literature studies. Classification results demonstrate that higher accuracy rates are obtained with the proposed methods compared to the other methods for all datasets.

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Section: Relief-fmentioning

confidence: 99%

Gene expression data classification using genetic algorithm-based feature selection

SÖNMEZ¹,

Dağtekin²,

Ensari³

2021

Turk J Elec Eng & Comp Sci

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“…Di samping algoritma SVM, algoritma klasifikasi lain yang juga memberikan hasil yang baik dalam permasalahan klasifikasi kanker adalah Extreme Learning Machine (ELM) [6] [7] dan k-Nearest Neighbor (kNN) [8] [9]. ELM adalah algoritma pembelajaran untuk single hidden layer feedforward network yang lebih baik dari algoritma gradient descent learning karena memerlukan waktu pelatihan yang lebih singkat, jumlah parameter pelatihan yang lebih sedikit dan kemampuan generalisasi yang lebih baik [10] [11].…”

Section: Pendahuluanunclassified

“…Untuk kasus non-linearly separable data, maka data akan dipetakan terlebih dahulu ke dimensi yang lebih tinggi menggunakan non-linear mapping. Nonlinear mapping ϕ(X 1 ) dapat diterapkan menggunakan fungsi kernel seperti persamaan (7). Beberapa pilihan fungsi kernel yang dapat digunakan antara lain linear, Gaussian atau radial basis function.…”

Section: Support Vector Machineunclassified

Ensemble Classifier untuk Klasifikasi Kanker Payudara

Khadijah

Kusumaningrum

2019

ITJRD

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Kanker payudara merupakan jenis kanker yang paling banyak diderita oleh kaum wanita di Indonesia. Penyakit tersebut dapat berakibat pada kematian jika terlambat ditangani. Oleh karena itu, deteksi dini kanker payudara merupakan langkah awal untuk menyelamatkan nyawa pasien. Pada penelitian ini telah dilakukan klasifikasi kanker payudara berdasarkan data anthopometric serta data dari hasil tes darah rutin menggunakan single classifier (ELM, SVM dan kNN) dan ensemble classifier yang menggabungkan ketiga algoritma tersebut dengan penentuan kelas majority voting. Pembagian data dilakukan dengan three way data split. Hasil eksperimen menunjukkan bahwa saat menggunakan keseluruhan fitur penggunaan ensemble classifier lebih baik daripada single classifier dalam hal akurasi maupun G-mean. Namun, saat menggunakan 4 fitur terbaik (resistin, glucose, age, dan BMI) penggunaan ensemble classifier sedikit lebih baik dalam hal G-mean. Hal ini disebabkan minimnya diversity di antara classifier sehingga saat digabungkan tidak mampu memperbaiki hasil.

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The Study of Synthetic Minority Over-sampling Technique (SMOTE) and Weighted Extreme Learning Machine for Handling Imbalance Problem on Multiclass Microarray classification

Khadijah

Endah

Kusumaningrum

et al. 2018

2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)

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Multiclass classification of cancer based on microarray data using extreme learning machine

Cited by 4 publications

References 20 publications

Gene expression data classification using genetic algorithm-based feature selection

Gene expression data classification using genetic algorithm-based feature selection

Ensemble Classifier untuk Klasifikasi Kanker Payudara

The Study of Synthetic Minority Over-sampling Technique (SMOTE) and Weighted Extreme Learning Machine for Handling Imbalance Problem on Multiclass Microarray classification

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