• Title/Summary/Keyword: Feature selection algorithm

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Performance Improvement of Freight Logistics Hub Selection in Thailand by Coordinated Simulation and AHP

  • Wanitwattanakosol, Jirapat;Holimchayachotikul, Pongsak;Nimsrikul, Phatchari;Sopadang, Apichat
    • Industrial Engineering and Management Systems
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    • v.9 no.2
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    • pp.88-96
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    • 2010
  • This paper presents a two-phase quantitative framework to aid the decision making process for effective selection of an efficient freight logistics hub from 8 alternatives in Thailand on the North-South economic corridor. Phase 1 employs both multiple regression and Pearson Feature selection to find the important criteria, as defined by logistics hub score, and to reduce number of criteria by eliminating the less important criteria. The result of Pearson Feature selection indicated that only 5 of 15 criteria affected the logistics hub score. Moreover, Genetic Algorithm (GA) was constructed from original 15 criteria data set to find the relationship between logistics criteria and freight logistics hub score. As a result, the statistical tools are provided the same 5 important criteria, affecting logistics hub score from GA, and data mining tool. Phase 2 performs the fuzzy stochastic AHP analysis with the five important criteria. This approach could help to gain insight into how the imprecision in judgment ratios may affect their alternatives toward the best solution and how the best alternative may be identified with certain confidence. The main objective of the paper is to find the best alternative for selecting freight logistics hub under proper criteria. The experimental results show that by using this approach, Chiang Mai province is the best place with the confidence interval 95%.

Variable Selection of Feature Pattern using SVM-based Criterion with Q-Learning in Reinforcement Learning (SVM-기반 제약 조건과 강화학습의 Q-learning을 이용한 변별력이 확실한 특징 패턴 선택)

  • Kim, Chayoung
    • Journal of Internet Computing and Services
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    • v.20 no.4
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    • pp.21-27
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    • 2019
  • Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

A Feature Set Selection Approach Based on Pearson Correlation Coefficient for Real Time Attack Detection (실시간 공격 탐지를 위한 Pearson 상관계수 기반 특징 집합 선택 방법)

  • Kang, Seung-Ho;Jeong, In-Seon;Lim, Hyeong-Seok
    • Convergence Security Journal
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    • v.18 no.5_1
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    • pp.59-66
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    • 2018
  • The performance of a network intrusion detection system using the machine learning method depends heavily on the composition and the size of the feature set. The detection accuracy, such as the detection rate or the false positive rate, of the system relies on the feature composition. And the time it takes to train and detect depends on the size of the feature set. Therefore, in order to enable the system to detect intrusions in real-time, the feature set to beused should have a small size as well as an appropriate composition. In this paper, we show that the size of the feature set can be further reduced without decreasing the detection rate through using Pearson correlation coefficient between features along with the multi-objective genetic algorithm which was used to shorten the size of the feature set in previous work. For the evaluation of the proposed method, the experiments to classify 10 kinds of attacks and benign traffic are performed against NSL_KDD data set.

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A Feature Analysis of Industrial Accidents Using C4.5 Algorithm (C4.5 알고리즘을 이용한 산업 재해의 특성 분석)

  • Leem, Young-Moon;Kwag, Jun-Koo;Hwang, Young-Seob
    • Journal of the Korean Society of Safety
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    • v.20 no.4 s.72
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    • pp.130-137
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    • 2005
  • Decision tree algorithm is one of the data mining techniques, which conducts grouping or prediction into several sub-groups from interested groups. This technique can analyze a feature of type on groups and can be used to detect differences in the type of industrial accidents. This paper uses C4.5 algorithm for the feature analysis. The data set consists of 24,887 features through data selection from total data of 25,159 taken from 2 year observation of industrial accidents in Korea For the purpose of this paper, one target value and eight independent variables are detailed by type of industrial accidents. There are 222 total tree nodes and 151 leaf nodes after grouping. This paper Provides an acceptable level of accuracy(%) and error rate(%) in order to measure tree accuracy about created trees. The objective of this paper is to analyze the efficiency of the C4.5 algorithm to classify types of industrial accidents data and thereby identify potential weak points in disaster risk grouping.

Exploring the Performance of Multi-Label Feature Selection for Effective Decision-Making: Focusing on Sentiment Analysis (효과적인 의사결정을 위한 다중레이블 기반 속성선택 방법에 관한 연구: 감성 분석을 중심으로)

  • Jong Yoon Won;Kun Chang Lee
    • Information Systems Review
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    • v.25 no.1
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    • pp.47-73
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    • 2023
  • Management decision-making based on artificial intelligence(AI) plays an important role in helping decision-makers. Business decision-making centered on AI is evaluated as a driving force for corporate growth. AI-based on accurate analysis techniques could support decision-makers in making high-quality decisions. This study proposes an effective decision-making method with the application of multi-label feature selection. In this regard, We present a CFS-BR (Correlation-based Feature Selection based on Binary Relevance approach) that reduces data sets in high-dimensional space. As a result of analyzing sample data and empirical data, CFS-BR can support efficient decision-making by selecting the best combination of meaningful attributes based on the Best-First algorithm. In addition, compared to the previous multi-label feature selection method, CFS-BR is useful for increasing the effectiveness of decision-making, as its accuracy is higher.

CNN-based Android Malware Detection Using Reduced Feature Set

  • Kim, Dong-Min;Lee, Soo-jin
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.10
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    • pp.19-26
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    • 2021
  • The performance of deep learning-based malware detection and classification models depends largely on how to construct a feature set to be applied to training. In this paper, we propose an approach to select the optimal feature set to maximize detection performance for CNN-based Android malware detection. The features to be included in the feature set were selected through the Chi-Square test algorithm, which is widely used for feature selection in machine learning and deep learning. To validate the proposed approach, the CNN model was trained using 36 characteristics selected for the CICANDMAL2017 dataset and then the malware detection performance was measured. As a result, 99.99% of Accuracy was achieved in binary classification and 98.55% in multiclass classification.

Gene Selection Based on Support Vector Machine using Bootstrap (붓스트랩 방법을 활용한 SVM 기반 유전자 선택 기법)

  • Song, Seuck-Heun;Kim, Kyoung-Hee;Park, Chang-Yi;Koo, Ja-Yong
    • The Korean Journal of Applied Statistics
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    • v.20 no.3
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    • pp.531-540
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    • 2007
  • The recursive feature elimination for support vector machine is known to be useful in selecting relevant genes. Since the criterion for choosing relevant genes is the absolute value of a coefficient, the recursive feature elimination may suffer from a scaling problem. We propose a modified version of the recursive feature elimination algorithm using bootstrap. In our method, the criterion for determining relevant genes is the absolute value of a coefficient divided by its standard error, which accounts for statistical variability of the coefficient. Through numerical examples, we illustrate that our method is effective in gene selection.

Genetic Algorithm for Image Feature Selection (영상 특징 선택을 위한 유전 알고리즘)

  • Shin Youns-Geun;Park Sang-Sung;Jang Dong-Sik
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06b
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    • pp.193-195
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    • 2006
  • As multimedia information increases sharply, In image retrieval field the method that can analyze image data quickly and exactly is required. In the case of image data, because each data includes a lot of informations, between accuracy and speed of retrieval become trade-off. To solve these problem, feature vector extracting process that use Genetic Algorithm for implementing prompt and correct image clustering system in case of retrieval of mass image data is proposed. After extracting color and texture features, the representative feature vector among these features is extracted by using Genetic Algorithm.

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Improving the Performance of Korean Text Chunking by Machine learning Approaches based on Feature Set Selection (자질집합선택 기반의 기계학습을 통한 한국어 기본구 인식의 성능향상)

  • Hwang, Young-Sook;Chung, Hoo-jung;Park, So-Young;Kwak, Young-Jae;Rim, Hae-Chang
    • Journal of KIISE:Software and Applications
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    • v.29 no.9
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    • pp.654-668
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    • 2002
  • In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of "incremental usefulness" of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively.

Document Classification of Small Size Documents Using Extended Relief-F Algorithm (확장된 Relief-F 알고리즘을 이용한 소규모 크기 문서의 자동분류)

  • Park, Heum
    • The KIPS Transactions:PartB
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    • v.16B no.3
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    • pp.233-238
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    • 2009
  • This paper presents an approach to the classifications of small size document using the instance-based feature filtering Relief-F algorithm. In the document classifications, we have not always good classification performances of small size document included a few features. Because total number of feature in the document set is large, but feature count of each document is very small relatively, so the similarities between documents are very low when we use general assessment of similarity and classifiers. Specially, in the cases of the classification of web document in the directory service and the classification of the sectors that cannot connect with the original file after recovery hard-disk, we have not good classification performances. Thus, we propose the Extended Relief-F(ERelief-F) algorithm using instance-based feature filtering algorithm Relief-F to solve problems of Relief-F as preprocess of classification. For the performance comparison, we tested information gain, odds ratio and Relief-F for feature filtering and getting those feature values, and used kNN and SVM classifiers. In the experimental results, the Extended Relief-F(ERelief-F) algorithm, compared with the others, performed best for all of the datasets and reduced many irrelevant features from document sets.