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  • Title/Summary/Keyword: Feature selection algorithm

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Decision Tree with Optimal Feature Selection for Bearing Fault Detection

  • Nguyen, Ngoc-Tu;Lee, Hong-Hee
    • Journal of Power Electronics
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    • v.8 no.1
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    • pp.101-107
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    • 2008
  • In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

AUTOMATIC SELECTION AND ADJUSTMENT OF FEATURES FOR IMAGE CLASSIFICATION

  • Saiki, Kenji;Nagao, Tomoharu
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2009.01a
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    • pp.525-528
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    • 2009
  • Recently, image classification has been an important task in various fields. Generally, the performance of image classification is not good without the adjustment of image features. Therefore, it is desired that the way of automatic feature extraction. In this paper, we propose an image classification method which adjusts image features automatically. We assume that texture features are useful in image classification tasks because natural images are composed of several types of texture. Thus, the classification accuracy rate is improved by using distribution of texture features. We obtain texture features by calculating image features from a current considering pixel and its neighborhood pixels. And we calculate image features from distribution of textures feature. Those image features are adjusted to image classification tasks using Genetic Algorithm. We apply proposed method to classifying images into "head" or "non-head" and "male" or "female".

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Searching for Optimal Ensemble of Feature-classifier Pairs in Gene Expression Profile using Genetic Algorithm (유전알고리즘을 이용한 유전자발현 데이타상의 특징-분류기쌍 최적 앙상블 탐색)

  • 박찬호;조성배
    • Journal of KIISE:Software and Applications
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    • v.31 no.4
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    • pp.525-536
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    • 2004
  • Gene expression profile is numerical data of gene expression level from organism, measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify disease with gene expression profile. Because all genes are not related to disease, it is needed to select related genes that is called feature selection, and it is needed to classify selected genes properly. This paper Proposes GA based method for searching optimal ensemble of feature-classifier pairs that are composed with seven feature selection methods based on correlation, similarity, and information theory, and six representative classifiers. In experimental results with leave-one-out cross validation on two gene expression Profiles related to cancers, we can find ensembles that produce much superior to all individual feature-classifier fairs for Lymphoma dataset and Colon dataset.

Feature selection for text data via sparse principal component analysis (희소주성분분석을 이용한 텍스트데이터의 단어선택)

  • Won Son
    • The Korean Journal of Applied Statistics
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    • v.36 no.6
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    • pp.501-514
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    • 2023
  • When analyzing high dimensional data such as text data, if we input all the variables as explanatory variables, statistical learning procedures may suffer from over-fitting problems. Furthermore, computational efficiency can deteriorate with a large number of variables. Dimensionality reduction techniques such as feature selection or feature extraction are useful for dealing with these problems. The sparse principal component analysis (SPCA) is one of the regularized least squares methods which employs an elastic net-type objective function. The SPCA can be used to remove insignificant principal components and identify important variables from noisy observations. In this study, we propose a dimension reduction procedure for text data based on the SPCA. Applying the proposed procedure to real data, we find that the reduced feature set maintains sufficient information in text data while the size of the feature set is reduced by removing redundant variables. As a result, the proposed procedure can improve classification accuracy and computational efficiency, especially for some classifiers such as the k-nearest neighbors algorithm.

Low-complexity Joint Transmit/Receive Antenna Selection Algorithm for Multi-Antenna Systems (다중 안테나 시스템을 위한 낮은 복잡도의 송/수신안테나 선택 알고리즘)

  • Son, Jun-Ho;Kang, Chung-G.
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.10A
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    • pp.943-951
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    • 2006
  • Multi-input-multi-output (MIMO) systems are considered to improve the capacity and reliability of next generation mobile communication. However, the multiple RF chains associated with multiple antennas are costly in terms of size, power and hardware. Antenna selection is a low-cost low-complexity alternative to capture many of the advantages of MIMO systems. We proposed new joint Tx/Rx antenna selection algorithm with low complexity. The proposed algorithm is a method selects LR×LT channel matrix out of LR×LT entire channel gain matrix where LR×LT matrix selects alternate Tx antenna with Rx antenna which have the largest channel gain to maximize Frobenius norm. The feature of this algorithm is very low complexity compare with Exhaustive search which have optimum capacity. In case of 4×4 antennas selection out of 8×8 antennas, the capacity decreases 0.52dB but the complexity also decreases about 1/10,000 than optimum exhaustive search.

Fuzzy Reasoning based Selection Operator for Genetic Algorithm (퍼지 추론 기반의 유전알고리즘 선택 연산자)

  • Seo, Ki-Sung;Hyun, Soo-Hwan
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.1
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    • pp.116-121
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    • 2008
  • This paper introduces a selection operator which utilized similarity and fitness of individuals based on fuzzy inference. Adding similarity feature to fitness, proposed selector obtained the decrease of premature convergence and better performances than other selectors. Moreover, an adoption of steady-state evolution provided enhancement of performances additionally. Experiments of proposed method for deceptive problems were tested and showed better performances than conventional methods.

Neuro-Fuzzy Network-based Depression Diagnosis Algorithm Using Optimal Features of HRV (뉴로-퍼지 신경망 기반 최적의 HRV특징을 이용한 우울증진단 알고리즘)

  • Zhang, Zhen-Xing;Tian, Xue-Wei;Lim, Joon-S.
    • The Journal of the Korea Contents Association
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    • v.12 no.2
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    • pp.1-9
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    • 2012
  • This paper presents an algorithm for depression diagnosis using the Neural Network with Weighted Fuzzy Membership functions (NEWFM) and heart rate variability (HRV). In the algorithm, 22 different features were initially extracted from the HRV signal by frequency domain, time domain, wavelet transformed, and Poincarˊe transformed feature extraction methods; of these 6 optimal features were selected by significance evaluation using Non-overlap Area Distribution Measurement (NADM) based on NEWFM. The proposed algorithm uses these 6 optimal features to diagnose depression with an accuracy of 95.83%.

Unsupervised Feature Selection Method Using a Fuzzy-Genetic Algorithm (퍼지-유전자 알고리즘을 이용한 무감독 특징 선택 방법)

  • 이영제;이정훈
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2000.05a
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    • pp.199-202
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    • 2000
  • 본 논문에서는 퍼지-유전자 접근방법을 이용한 무감독 특징 선택방법에 대하여 나타내었다. 이 방법은 각각의 특징들의 중요도에 따라 순서를 정하기 위해 사용되는 weighted distance 를 포함하는 특징 평가 지표 (feature evaluation index)를 최소화시키는데 있다. 또한 특징 평가 지표에서 사용되는 각 패턴들의 쌍에 대하여 근접함의 정도를 퍼지 멤버쉽 함수를 이용하여 결정하고 유전자 알고리즘은 평가 지표를 최소화시킴으로써 각 특징의 중요도를 나타내는 최적의 weighting 계수의 집합을 한기 위하여 적용하였다.

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Development of Classification Model for hERG Ion Channel Inhibitors Using SVM Method (SVM 방법을 이용한 hERG 이온 채널 저해제 예측모델 개발)

  • Gang, Sin-Moon;Kim, Han-Jo;Oh, Won-Seok;Kim, Sun-Young;No, Kyoung-Tai;Nam, Ky-Youb
    • Journal of the Korean Chemical Society
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    • v.53 no.6
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    • pp.653-662
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    • 2009
  • Developing effective tools for predicting absorption, distribution, metabolism, excretion properties and toxicity (ADME/T) of new chemical entities in the early stage of drug design is one of the most important tasks in drug discovery and development today. As one of these attempts, support vector machines (SVM) has recently been exploited for the prediction of ADME/T related properties. However, two problems in SVM modeling, i.e. feature selection and parameters setting, are still far from solved. The two problems have been shown to be crucial to the efficiency and accuracy of SVM classification. In particular, the feature selection and optimal SVM parameters setting influence each other, which indicates that they should be dealt with simultaneously. In this account, we present an integrated practical solution, in which genetic-based algorithm (GA) is used for feature selection and grid search (GS) method for parameters optimization. hERG ion-channel inhibitor classification models of ADME/T related properties has been built for assessing and testing the proposed GA-GS-SVM. We generated 6 different models that are 3 different single models and 3 different ensemble models using training set - 1891 compounds and validated with external test set - 175 compounds. We compared single model with ensemble model to solve data imbalance problems. It was able to improve accuracy of prediction to use ensemble model.

Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms

  • Eo, Kyun Sun;Lee, Kun Chang
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.11
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    • pp.41-50
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    • 2020
  • Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.