Formulating Particle Swarm Optimization based Generalized Kernel Function for Kernel-Linear Discriminant Analysis

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.

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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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Target Threat Assessment in Air Combat Based on Improved Glowworm Swarm Optimization and ELM Neural Network

International Journal of Aerospace Engineering ◽

10.1155/2021/4687167 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Yuan Cao ◽

Ying-Xin Kou ◽

An Xu ◽

Zhi-Fei Xi

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Objective Assessment ◽

Threat Assessment ◽

Extreme Learning Machines ◽

Swarm Optimization ◽

Linear Discriminant ◽

Learning Machines ◽

Glowworm Swarm Optimization ◽

Air Combat

Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats.

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Particle swarm optimization based parameter selection technique for unsupervised discriminant analysis in transfer learning framework

Applied Intelligence ◽

10.1007/s10489-020-01710-7 ◽

2020 ◽

Vol 50 (10) ◽

pp. 3071-3089

Author(s):

Rakesh Kumar Sanodiya ◽

Jimson Mathew ◽

Sriparna Saha ◽

Piyush Tripathi

Keyword(s):

Particle Swarm Optimization ◽

Discriminant Analysis ◽

Transfer Learning ◽

Particle Swarm ◽

Parameter Selection ◽

Swarm Optimization ◽

Selection Technique ◽

Learning Framework

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