scholarly journals KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019)

2020 ◽  
Vol 8 (2) ◽  
pp. 88
Author(s):  
Amanah Saeroni ◽  
Memi Nor Hayati ◽  
Rito Goejantoro
Keyword(s):  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Customer Data ◽  
Independent Variables ◽  
Premium Payment ◽  
Multivariate Technique ◽  
Fisher Discriminant

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%.

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

Fuzzy Maximum Scatter Discriminant Analysis and its Application to Face Recognition

2011 ◽  
Vol 317-319 ◽  
pp. 150-153
Author(s):  
Wan Li Feng ◽  
Shang Bing Gao
Keyword(s):  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Small Sample Size ◽  
Small Sample ◽  
K Nearest Neighbor ◽  
Small Sample Size Problem ◽  
Fisher Discriminant Analysis ◽  
Singularity Problem ◽  
Scatter Matrix ◽  
Fisher Discriminant

In this paper, a reformative scatter difference discriminant criterion (SDDC) with fuzzy set theory is studied. The scatter difference between between-class and within-class as discriminant criterion is effective to overcome the singularity problem of the within-class scatter matrix due to small sample size problem occurred in classical Fisher discriminant analysis. However, the conventional SDDC assumes the same level of relevance of each sample to the corresponding class. So, a fuzzy maximum scatter difference analysis (FMSDA) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the distribution information of original samples, and this information is utilized to redefine corresponding scatter matrices which are different to the conventional SDDC and effective to extract discriminative features from overlapping (outlier) samples. Experiments conducted on FERET face databases demonstrate the effectiveness of the proposed method.

FAULT DIAGNOSIS BASED ON MULTI-SCALE CLASSIFICATION USING KERNEL FISHER DISCRIMINANT ANALYSIS AND GAUSSIAN MIXTURE MODEL AND K-NEAREST NEIGHBOR METHOD

2017 ◽  
Vol 79 (5-3) ◽  
Author(s):  
Norazwan Md Nor ◽  
Mohd Azlan Hussain ◽  
Che Rosmani Che Hassan
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Gaussian Mixture ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Multi Scale ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Effective fault monitoring, detection and diagnosis of chemical processes is important to ensure the consistency and high product quality, as well as the safety of the processes. Fault diagnosis problems can be considered as classification problems as these techniques have been proposed and greatly improved over the past few years. However, a chemical process is often characterized by large scale and non-linear behavior. When linear discriminant analysis is used for fault diagnosis in the system, a lot of incorrect diagnosis will occur. As solution, this paper presents a novel approach for feature extraction and classification framework in chemical process systems based on wavelet transformation and discriminant analysis. The proposed multi-scale kernel Fisher discriminant analysis (MSKFDA) method used the combination of kernel Fisher discriminant analysis (KFDA) and discrete wavelet transform (DWT) to improve the classification performance as compared to conventional approaches. A DWT is applied to extract the process dynamics at different scales by decomposed the data into multiple scales, analyzed by the KFDA and only dynamical characteristics with important information was reconstructed by inverse discrete wavelet transform (IDWT). Then, Gaussian mixture model (GMM) and K-nearest neighbor (KNN) method were individually applied for the fault classification using the output from the MSKFDA approach. These two classifiers are evaluated and compared based on their performance on the Tennessee Eastman process database. The proposed framework for GMM and KNN classifiers had achieved average classification accuracies of 84.72% and 82.00%, respectively, with the results show significant improvement over existing methods in fault detection and classification.

Two-parameter KNN algorithm and its application in recognition of brand rice

2021 ◽  
pp. 1-7
Author(s):  
Zhu Siyu ◽  
He Chongnan ◽  
Song Mingjuan ◽  
Li Linna
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Pattern Class ◽  
Fisher Discriminant ◽  
Two Parameter

In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

scholarly journals Alifta Ainurrochmah, Perbandinga

2020 ◽  
Vol 11 (2) ◽  
pp. 37
Author(s):  
Alifta Ainurrochmah ◽  
Memi Nor Hayati ◽  
Andi M. Ade Satriya
Keyword(s):  
Discriminant Analysis ◽  
Naive Bayes ◽  
Insurance Premium ◽  
Naïve Bayes ◽  
Fisher Discriminant Analysis ◽  
Bayes Method ◽  
Premium Payment ◽  
Multivariate Technique ◽  
Fisher’S Discriminant Analysis ◽  
Naive Bayes Method

Classification is a technique to form a model of data that is already known to its classification group. The model was formed will be used to classify new objects. Fisher discriminant analysis is multivariate technique to separate objects in different groups. Naive Bayes is a classification technique based on probability and Bayes theorem with assumption of independence. This research has a goal to compare the level of classification accuracy between Fisher's discriminant analysis and Naive Bayes method on the insurance premium payment status customer. The data used four independent variables that is income, age, premium payment period and premium payment amount. The results of misclassification using the APER (Apparent Rate Error) indicate that the naive Bayes method has a higher level of accuracy is 15,38% than Fisher’s discriminant analysis is 46,15% on the insurance premium payment status customer.

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