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  • Title/Summary/Keyword: Feature selection algorithm

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Effective Dimensionality Reduction of Payload-Based Anomaly Detection in TMAD Model for HTTP Payload

  • Kakavand, Mohsen;Mustapha, Norwati;Mustapha, Aida;Abdullah, Mohd Taufik
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.8
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    • pp.3884-3910
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    • 2016
  • Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

Feature Points Selection Using Block-Based Watershed Segmentation and Polygon Approximation (블록기반 워터쉐드 영역분할과 다각형 근사화를 이용한 특징점 추출)

  • 김영덕;백중환
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2000.12a
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    • pp.93-96
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    • 2000
  • In this paper, we suggest a feature points selection method using block-based watershed segmentation and polygon approximation for preprocessing of MPEG-4 mesh generation. 2D natural image is segmented by 8×8 or 4×4 block classification method and watershed algorithm. As this result, pixels on the watershed lines represent scene's interior feature and this lines are shapes of closed contour. Continuous pixels on the watershed lines are selected out feature points using Polygon approximation and post processing.

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A MA-plot-based Feature Selection by MRMR in SVM-RFE in RNA-Sequencing Data

  • Kim, Chayoung
    • The Journal of Korean Institute of Information Technology
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    • v.16 no.12
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    • pp.25-30
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    • 2018
  • It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.

A Hybrid Soft Computing Technique for Software Fault Prediction based on Optimal Feature Extraction and Classification

  • Balaram, A.;Vasundra, S.
    • International Journal of Computer Science & Network Security
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    • v.22 no.5
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    • pp.348-358
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    • 2022
  • Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

Microscopic Image-based Cancer Cell Viability-related Phenotype Extraction (현미경 영상 기반 암세포 생존력 관련 표현형 추출)

  • Misun Kang
    • Journal of Biomedical Engineering Research
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    • v.44 no.3
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    • pp.176-181
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    • 2023
  • During cancer treatment, the patient's response to drugs appears differently at the cellular level. In this paper, an image-based cell phenotypic feature quantification and key feature selection method are presented to predict the response of patient-derived cancer cells to a specific drug. In order to analyze the viability characteristics of cancer cells, high-definition microscope images in which cell nuclei are fluorescently stained are used, and individual-level cell analysis is performed. To this end, first, image stitching is performed for analysis of the same environment in units of the well plates, and uneven brightness due to the effects of illumination is adjusted based on the histogram. In order to automatically segment only the cell nucleus region, which is the region of interest, from the improved image, a superpixel-based segmentation technique is applied using the fluorescence expression level and morphological information. After extracting 242 types of features from the image through the segmented cell region information, only the features related to cell viability are selected through the ReliefF algorithm. The proposed method can be applied to cell image-based phenotypic screening to determine a patient's response to a drug.

Speech emotion recognition based on genetic algorithm-decision tree fusion of deep and acoustic features

  • Sun, Linhui;Li, Qiu;Fu, Sheng;Li, Pingan
    • ETRI Journal
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    • v.44 no.3
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    • pp.462-475
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    • 2022
  • Although researchers have proposed numerous techniques for speech emotion recognition, its performance remains unsatisfactory in many application scenarios. In this study, we propose a speech emotion recognition model based on a genetic algorithm (GA)-decision tree (DT) fusion of deep and acoustic features. To more comprehensively express speech emotional information, first, frame-level deep and acoustic features are extracted from a speech signal. Next, five kinds of statistic variables of these features are calculated to obtain utterance-level features. The Fisher feature selection criterion is employed to select high-performance features, removing redundant information. In the feature fusion stage, the GA is is used to adaptively search for the best feature fusion weight. Finally, using the fused feature, the proposed speech emotion recognition model based on a DT support vector machine model is realized. Experimental results on the Berlin speech emotion database and the Chinese emotion speech database indicate that the proposed model outperforms an average weight fusion method.

A Novel Image Classification Method for Content-based Image Retrieval via a Hybrid Genetic Algorithm and Support Vector Machine Approach

  • Seo, Kwang-Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.10 no.3
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    • pp.75-81
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    • 2011
  • This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

Classification of TV Program Scenes Based on Audio Information

  • Lee, Kang-Kyu;Yoon, Won-Jung;Park, Kyu-Sik
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.3E
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    • pp.91-97
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    • 2004
  • In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

Feature selection using genetic algorithm for constructing time-series modelling

  • Oh, Sang-Keon;Hong, Sun-Gi;Kim, Chang-Hyun;Lee, Ju-Jang
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.102.4-102
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    • 2001
  • An evolutionary structure optimization method for the Gaussian radial basis function (RBF) network is presented, for modelling and predicting nonlinear time series. Generalization performance is significantly improved with a much smaller network, compared with that of the usual clustering and least square learning method.

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A Genetic Algorithm for Clustering in Data Mining

  • 정지원;최인찬
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2000.04a
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    • pp.306-309
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    • 2000
  • 본 논문에서는 데이터마이닝 문제에 클러스터링 기법을 적용할 때 발생할 수 있는 문제점 및 속성선택(feature selection)과 그룹 수 산정의 상호연관성을 살펴보고, 데이터 필드의 상대적 중요도와 최적의 그룹 수를 결정하는 수리적 모형을 제시한다. 또한, 이 모형을 풀기 위하여 K-means 알고리즘을 이용한 유전 알고리즘을 제시한다.

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