• Tidak ada hasil yang ditemukan

J00143

N/A
N/A
Info
Unduh - Bebas
Protected

Academic year: 2017

Membagikan " J00143"

Copied!
15
0
0

Memuat.... (Lihat teks lengkap sekarang)

Unduh sekarang ( 15 Halaman )

Teks penuh

Loading

Gambar

Figure 1 Confusion matrix
Figure 2 A flow chart showing the grid algorithm for SVM, NN, and SVDD classifiers
Figure 3 A flow chart of the main programme for SVDD, SVM, and NN classifiers with feature selection
Table 3 Computational time for the classifier without feature selection
+4

Referensi

Unduh sekarang ( PDF - 15 Halaman - 103.13 KB )

Garis besar

J00143

Dokumen terkait

Optimizing SVR using Local Best PSO for Software Effort Estimation

Keywords: Optimization, SVR, Optimal Parameter, Feature Selection, Local Best PSO, Software Effort Estimation.. 1

Data Mining pada Penentuan Kelayakan Kredit Menggunakan Algoritma K-nn Berbasis Forward Selection Data Mining On Credit Feasibility Determination Using K-nn Algorithm Based On Forward Selection

Klasifikasi risiko penentun kelayakan kredit menggunakan algoritma K-NN berbasis Forward Selection telah dilakukan dengan hasil akurasi 73,60%. Hasil ini diperoleh

Conference paper

In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate

On the Use of the Genetic Algorithm Filt

A search-based feature selection is used to find the opti- mal feature subset. It also removes the irrelevant features and finds the best subset that maximizes the fitness function.

Analisis Sentimen Penerapan Kurikulum Merdeka Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor Dengan Forward Selection

Metode klasifikasi yang digunakan untuk mengetahui Analisis Sentimen pada kurikulum Merdeka Belajar adalah metode K-Nearest Neighbor (K-NN) dengan Forward Selection (FS)..

Contoh Tugas Laporan Data Mining

Dalam penelitian ini digunakan forward feature selection (FFS) dan backward feature elimination (BFE) sebagai seleksi fitur berbasis wrapper. FFS dan BFE mempunyai tujuan yang

PDF Privacy preservation in data intensive environment - Taylor's

Proposed work Here using the method of feature selection for getting the best feature set to be selected for privacy preservation by using PCA Principle Component Analysis Hasan, &

An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance

Performance accuracy of BESTrees auto feature selection and single classifiers 5 Conclusion This study proposed an optimized bagging ensemble framework by analyzing vari- ous