Normalised Local Naïve Bayes Nearest-Neighbour Classifier for Offline Writer Identification

2017 ◽  
Author(s):  
Hussein Mohammed ◽  
Volker Maergner ◽  
Thomas Konidaris ◽  
H. Siegfried Stiehl
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Nearest Neighbour ◽  
Writer Identification ◽  
Nearest Neighbour Classifier

Spatial assessment of PM10 hotspots using Random Forest, K-Nearest Neighbour and Naïve Bayes

2021 ◽  
Vol 12 (10) ◽  
pp. 101202 ◽  
Author(s):  
Abdulwaheed Tella ◽  
Abdul-Lateef Balogun ◽  
Naheem Adebisi ◽  
Samsuri Abdullah
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Nearest Neighbour ◽  
Spatial Assessment

scholarly journals PENERAPAN ALGORITMA K-MEANS PADA SISWA BARU SEKOLAHMENENGAH KEJURUAN UNTUK CLUSTERING JURUSAN

2017 ◽  
Vol 1 (2) ◽  
pp. 100-105 ◽  
Author(s):  
Fauziah Nur ◽  
M. Zarlis ◽  
Benny Benyamin Nasution
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Nearest Neighbour ◽  
Knowledge Discovery In Database ◽  
Cluster 2

Data mining merupakan teknik pengolahan data dalam jumlah besar untuk pengelompokan.Teknik ini digunakan dalam proses Knowledge Discovery in Database (KDD). Teknik tersebut mempunyai beberapa metode dalam pengelompokannya Naïve-Bayes dan Nearest Neighbour, pohon keputusan (KD-Tree), ID3, K-Means, text mining dan dbscan. Dalam hal ini penulis mengelompokan data siswa baru sekolah menengah kejuruan tahun ajaran 2014/2015. Pengelompokan tersebut berdasarkan kriteria – kriteria data siswa. Pada penelitian ini, penulis menerapkan algoritma K-Means Clustering untuk pengelompokan data siswa baru sekolah menengah kejuruan. Dalam hal ini, pada umumnya untuk memamasuki jurusan hanya disesuaikan dengan nilai siswa saja namun dalam penelitian ini pengelompokan disesuaikan kriteria – kriteria siswa seperti penghasilan orang tua, tanggungan anak orang tua dan nilai tes siswa. Penulis menggunakan beberapa kriteria tersebut agar pengelompokan yang dihasilkan menjadi lebih optimal. Tujuan dari pengelompokan ini adalah terbentuknya kelompok jurusan pada siswa yang menggunakan algoritma K-Means clustering. Hasil dari pengelompokan tersebut diperoleh tiga kelompok yaitu kelompok tidak lulus, kelompok rekayasa perangkat lunak dan kelompok teknik komputer jaringan. Terdapat pusat cluster  dengan Cluster-1=1.4;2.2;2.2, Cluster-2= 2.28;1.64;4 dan Cluster-3=5;2;6. Pusat cluster tersebut didapat dari beberapa iterasi sehingga mengahasilakan pusat cluster yang optimal.

scholarly journals Writer Identification Using Handwritten Cursive Texts and Single Character Words

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 391 ◽  
Author(s):  
Tobias Kutzner ◽  
Carlos Pazmiño-Zapatier ◽  
Matthias Gebhard ◽  
Ingrid Bönninger ◽  
Wolf-Dietrich Plath ◽  
...  
Keyword(s):  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Feature Reduction ◽  
Writer Identification ◽  
K Nearest Neighbor ◽  
Fisher Score ◽  
Single Character ◽  
Attribute Evaluation ◽  
Online Handwriting

One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.

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