Support Vector Machines, Kernel Logistic Regression and Boosting

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

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Efficient and Private Scoring of Decision Trees, based on Pre-Computation Technique with Support Vector Machines and Logistic Regression Model

International Journal of Computing Communications and Networking ◽

10.30534/ijccn/2018/16722018 ◽

2018 ◽

Vol 7 (2) ◽

pp. 96-99

Author(s):

Keyword(s):

Logistic Regression ◽

Support Vector Machines ◽

Regression Model ◽

Decision Trees ◽

Logistic Regression Model ◽

Support Vector ◽

Computation Technique ◽

Vector Machines

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Support vector machines versus logistic regression: improving prospective performance in clinical decision-making

Ultrasound in Obstetrics and Gynecology ◽

10.1002/uog.2791 ◽

2006 ◽

Vol 27 (6) ◽

pp. 607-608 ◽

Cited By ~ 25

Author(s):

N. L. M. M. Pochet ◽

J. A. K. Suykens

Keyword(s):

Decision Making ◽

Logistic Regression ◽

Support Vector Machines ◽

Clinical Decision Making ◽

Clinical Decision ◽

Support Vector ◽

Vector Machines

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MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR SEKTOR INDUSTRI BARANG DAN KONSUMSI

Teknika: Engineering and Sains Journal ◽

10.51804/tesj.v3i2.490.77-82 ◽

2019 ◽

Vol 3 (2) ◽

pp. 77

Author(s):

Herlina Herlina ◽

Ahmad Ridho’i ◽

Anggie Erma Yunita ◽

Mega Puja Azhari ◽

Ade Reynaldi Saputra

Keyword(s):

Logistic Regression ◽

Particle Swarm Optimization ◽

Support Vector Machines ◽

Discriminant Analysis ◽

Financial Distress ◽

Particle Swarm ◽

Support Vector ◽

Multiple Discriminant Analysis ◽

Swarm Optimization ◽

Vector Machines

Kesulitan keuangan (financial distress) adalah sebuah tahapan yang akan dilalui oleh sebuah perusahaan sebelum mengalami kebangkrutan. Dengan alasan tersebut maka kemampuan untuk memprediksi kesulitan keuangan dapat menjadi informasi yang bermanfaat bagi perusahaan maupun investor. Penelitian mengenai financial distress sudah dimulai dari penelitian Altman pada tahun 1968 menggunakan metode Multiple Discriminant Analysis (MDA). Dimulai dari penelitian Altman, muncul penelitian-penelitian lainnya menggunakan pengembangan metode statistik, seperti Logistic Regression. Dari metode statistik kemudian berkembang dengan munculnya penelitian-penelitian menggunakan metode-metode kecerdasan buatan, serta algoritma evolusi untuk berusaha mendapatkan model prediksi financial distress yang akurat. Tujuan dari penelitian ini adalah untuk membandingkan tingkat akurasi dari model prediksi financial distress perusahaan manufaktur terbuka pada sektor industri barang konsumsi yang terdaftar pada Bursa Efek Indonesia menggunakan metode kecerdasan buatan serta algoritma evolusi. Metode yang digunakan untuk metode kecerdasan buatan adalah metode Support Vector Machines dan untuk model algoritma evolusi menggunakan metode Particle Swarm Optimization-Support Vector Machines. Tingkat akurasi dari masing-masing metode akan diukur dari prosentase misklasifikasi terkecil yang dihasilkan. Dari pengujian model menggunakan metode Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 11,11% dengan menggunakan Kernel Linear dan untuk metode Particle Swarm Optimization-Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 5,56% dengan menggunakan Kernel RBF, ? = 2.

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