Integrasi Metode Sample Bootstrapping dan Weighted Principal Component Analysis untuk Meningkatkan Performa K Nearest Neighbor pada Dataset Besar

Algoritma k Nearest Neighbor (kNN) merupakan metode untuk melakukan klasifikasi terhadap objek baru berdasarkan k tetangga terdekatnya. Algoritma kNN memiliki kelebihan karena sederhana, efektif dan telah banyak digunakan pada banyak masalah klasifikasi. Namun algoritma kNN memiliki kelemahan jika digunakan pada dataset yang besar karena membutuhkan waktu komputasi cukup tinggi. Pada penelitian ini integrasi metode Sample Bootstrapping dan Weighted Principal Component Analysis (PCA) diusulkan untuk meningkatkan akurasi dan waktu komputasi yang optimal pada algoritma kNN. Metode Sample Bootstrapping digunakan untuk mengurangi jumlah data training yang akan diproses. Metode Weighted PCA digunakan dalam mengurangi atribut. Dalam penelitian ini menggunakan dataset yang memiliki dataset training yang besar yaitu Landsat Satellite sebesar 4435 data dan Tyroid sebesar 3772 data. Dari hasil penelitian, integrasi algoritma kNN dengan Sample Bootstrapping dan Weighted PCA pada dataset Landsat Satellite akurasinya meningkat 0.77% (91.40%-90.63%) dengan selisih waktu 9 (1-10) detik dibandingkan algoritma kNN standar. Integrasi algoritma kNN dengan Sample Bootstrapping dan Weighted PCA pada dataset Thyroid akurasinya meningkat 3.10% (89.31%-86.21%) dengan selisih waktu 11 (1-12) detik dibandingkan algoritma kNN standar. Dari hasil penelitian yang dilakukan, dapat disimpulkan bahwa integrasi algoritma kNN dengan Sample Bootstrapping dan Weighted PCA menghasilkan akurasi dan waktu komputasi yang lebih baik daripada algoritma kNN standar

An improved multiple classifier combination scheme for pattern classification

Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. The main problem of multiple classifier combination is that there is no standard guideline for constructing an accurate and diverse classifier ensemble. This is due to the difficulty in identifying the number of homogeneous classifiers and how to combine the classifier outputs. The most commonly used ensemble method is the random strategy while the majority voting technique is used as the combiner. However, the random strategy cannot determine the number of classifiers and the majority voting technique does not consider the strength of each classifier, thus resulting in low classification accuracy. In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. A weighted voting technique is used to combine the classifier outputs by considering the strength of the classifiers prior to voting. Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. The average classification accuracy of the homogeneous NMC, NBC, k-NN and LDA ensembles are 97.91%, 98.06%, 98.09% and 98.12% respectively. The accuracies are higher than those obtained through the use of other approaches in developing multiple classifier combination. The proposed multiple classifier combination scheme will help to develop other multiple classifier combination for pattern recognition and classification

Ant system-based feature set partitioning algorithm for classifier ensemble construction

Ensemble method is considered as a new direction in pattern classification. Accuracy and diversity in a set of classifiers are two important things to be considered in constructing classifier ensemble.Several approaches have been proposed to construct the classifier ensemble. All of these approaches attempt to generate diversity in the ensemble.However, classifier ensemble construction still remains a problem because there is no standard guideline in constructing a set of accurate and diverse classifiers. In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. Experimental results on several datasets from University of California, Irvine have shown that the proposed algorithm has successfully constructed better classifier ensembles