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Wijaya, Adi
Universitas Gadjah Mada
Computer Science & IT Electrical & Electronics Engineering

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Two-Step Cluster based Feature Discretization of Naive Bayes for Outlier Detection in Intrinsic Plagiarism Detection Wijaya, Adi; Wahono, Romi Satria
Journal of Intelligent Systems Vol 1, No 1 (2015)
Publisher : IlmuKomputer.Com

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (622.156 KB)

Abstract

Intrinsic plagiarism detection is the task of analyzing a document with respect to undeclared changes in writing style which treated as outliers. Naive Bayes is often used to outlier detection. However, Naive Bayes has assumption that the values of continuous feature are normally distributed where this condition is strongly violated that caused low classification performance. Discretization of continuous feature can improve the performance of Naïve Bayes. In this study, feature discretization based on Two-Step Cluster for Naïve Bayes has been proposed. The proposed method using tf-idf and query language model as feature creator and False Positive/False Negative (FP/FN) threshold which aims to improve the accuracy and evaluated using PAN PC 2009 dataset. The result indicated that the proposed method with discrete feature outperform the result from continuous feature for all evaluation, such as recall, precision, f-measure and accuracy. The using of FP/FN threshold affects the result as well since it can decrease FP and FN; thus, increase all evaluation.
Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme Wijaya, Adi; Adji, Teguh Bharata; Setiawan, Noor Akhmad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.009 KB) | DOI: 10.11591/eecsi.v5.1665

Abstract

Achieving consistent accuracy still big challenge in EEG based Motor Imagery classification since the nature of EEG signal is non-stationary, intra-subject and inter-subject dependent. To address this problems, we propose the feature extraction scheme employing statistical measurements in narrow window with channel instantiation approach. In this study, k-Nearest Neighbor is used and a voting scheme as final decision where the most detection in certain class will be a winner. In this channel instantiation scheme, where EEG channel become instance or record, seventeen EEG channels with motor related activity is used to reduce from 118 channels. We investigate five narrow windows combination in the proposed methods, i.e.: one, two, three, four and five windows. BCI competition III Dataset IVa is used to evaluate our proposed methods. Experimental results show that one window with all channel and a combination of five windows with reduced channel outperform all prior research with highest accuracy and lowest standard deviation. This results indicate that our proposed methods achieve consistent accuracy and promising for reliable BCI systems.
Co-Authors Noor Akhmad Setiawan Romi Satria Wahono Teguh Bharata Adji