Classification Based on Configuration Objects by Using Procrustes Analysis

Abstract

Classification is one of the data mining topics that will predict an object to go into a certain group. The prediction process can be performed by using similarity measures, classification trees, or regression. On the other hand, Procrustes refers to a technique of matching two configurations that have been implemented for outlier detection. Based on the result, Procrustes has a potential to tackle the misclassification problem when the outliers are assumed as the misclassified object. Therefore, the Procrustes classification algorithm (PrCA) and Procrustes nearest neighbor classification algorithm (PNNCA) were proposed in this paper. The results of those algorithms had been compared to the classical classification algorithms, namely k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), AdaBoost (AB), Random Forest (RF), Logistic Regression (LR), and Ridge Regression (RR). The data used were iris, cancer, liver, seeds, and wine dataset. The minimum and maximum accuracy values obtained by the PrCA algorithm were 0.610 and 0.925, while the PNNCA were 0.610 and 0.963. PrCA was generally better than k-NN, SVM, and AB. Meanwhile, PNNCA was generally better than k-NN, SVM, AB, and RF. Based on the results, PrCA and PNNCA certainly deserve to be proposed as a new approach in the classification process.

Unsupervised Feature Selection Based on Self-configuration Approaches using Multidimensional Scaling

“…The KNN algorithm is popular enough in Machine Learning. And also, its classification process is based on the proximities of the object using their configuration [29].…”

Metode K-Nearest Neighbor Dan Fitur Warna Untuk Klasifikasi Daun Sirih Berdasarkan Citra Digital

“…F. Klasifikasi Klasifikasi adalah salah satu topik data mining yang paling populer untuk mencari informasi dan banyak digunakan untuk menentukan suatu keputusan dengan pengetahuan baru yang diperoleh dari pengolahan data masa lalu menggunakan suatu algoritma (Ananda & Prasetiadi, 2021;Handayani & Ikrimach, 2020;Nuklianggraita et al, 2020) Pada penelitian ini, proses klasifikasi dilakukan dengan menggunakan cross-validation untuk membagi dataset menjadi dua set yaitu data training dan data testing serta menerapkan metode klasifikasi KNN.…”