• Title, Summary, Keyword: Classification Algorithm

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Classification System Model Design for Algorithm Education for Elementary and Secondary Students (초중등학생 대상 알고리즘 교육을 위한 분류체계 모형 설계)

  • Lee, Young-ho;Koo, Duk-hoi
    • Journal of The Korean Association of Information Education
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    • v.21 no.3
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    • pp.297-307
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    • 2017
  • The purpose of this study is to propose algorithm classification system for algorithm education for Elementary and Secondary Students. We defines the components of the algorithm and expresses the algorithm classification system by the analysis synthesis method. The contents of the study are as follows. First, we conducted a theoretical search on the classification purpose and classification. Second, the contents and limitations of the classification system for the proposed algorithm contents were examined. In addition, we examined the contents and selection criteria of algorithms used in algorithm education research. Third, the algorithm components were redefined using the core idea and crosscutting concept proposed by the NRC. And the crosscutting concept of algorithm is subdivided into algorithm data structure and algorithm design strategy, and its contents are presented using analytic synthesis classification scheme. Finally, the validity of the proposed contents was verified by the review of the expert group. It is expected that the study on the algorithm classification system will provide many implications for the contents selection and training method in the algorithm education.

Pattern Classification of Multi-Spectral Satellite Images based on Fusion of Fuzzy Algorithms (퍼지 알고리즘의 융합에 의한 다중분광 영상의 패턴분류)

  • Jeon, Young-Joon;Kim, Jin-Il
    • Journal of KIISE:Software and Applications
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    • v.32 no.7
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    • pp.674-682
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    • 2005
  • This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.

Negative Selection Algorithm for DNA Pattern Classification

  • Lee, Dong-Wook;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.190-195
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    • 2004
  • We propose a pattern classification algorithm using self-nonself discrimination principle of immune cells and apply it to DNA pattern classification problem. Pattern classification problem in bioinformatics is very important and frequent one. In this paper, we propose a classification algorithm based on the negative selection of the immune system to classify DNA patterns. The negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes ${\eta}$ groups of antigenic receptor for ${\eta}$ different patterns, these receptor groups can classify into ${\eta}$ patterns. We propose a pattern classification algorithm based on the negative selection in nucleotide base level and amino acid level. Also to show the validity of our algorithm, experimental results of RNA group classification are presented.

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Scalable Packet Classification Algorithm through Mashing (Hashing을 사용한 Scalable Packet Classification 알고리즘 연구)

  • Heo, Jae-Sung;Choi, Lynn
    • Proceedings of the IEEK Conference
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    • pp.113-116
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    • 2002
  • It is required to network to make more intelligent packet processing and forwarding for increasing bandwidth and various services. Classification provides these intelligent to network which is acquired by increasing number of rules in classification rule set. In this Paper, we propose a classification algorithm efficient to scalable rule set ahead as well as Present small rule set. This algorithm has competition to existing methods by performance and advantage that it is mixed with another algorithm because il does not change original shape of rule set.

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Design and Performance Measurement of a Genetic Algorithm-based Group Classification Method : The Case of Bond Rating (유전 알고리듬 기반 집단분류기법의 개발과 성과평가 : 채권등급 평가를 중심으로)

  • Min, Jae-H.;Jeong, Chul-Woo
    • Journal of the Korean Operations Research and Management Science Society
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    • v.32 no.1
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    • pp.61-75
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    • 2007
  • The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.

New Automatic Taxonomy Generation Algorithm for the Audio Genre Classification (음악 장르 분류를 위한 새로운 자동 Taxonomy 구축 알고리즘)

  • Choi, Tack-Sung;Moon, Sun-Kook;Park, Young-Cheol;Youn, Dae-Hee;Lee, Seok-Pil
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.3
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    • pp.111-118
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    • 2008
  • In this paper, we propose a new automatic taxonomy generation algorithm for the audio genre classification. The proposed algorithm automatically generates hierarchical taxonomy based on the estimated classification accuracy at all possible nodes. The estimation of classification accuracy in the proposed algorithm is conducted by applying the training data to classifier using k-fold cross validation. Subsequent classification accuracy is then to be tested at every node which consists of two clusters by applying one-versus-one support vector machine. In order to assess the performance of the proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigated classification performance using the proposed algorithm and previous flat classifiers. The classification accuracy reaches to 89 percent with proposed scheme, which is 5 to 25 percent higher than the previous flat classification methods. Using low-dimensional feature vectors, in particular, it is 10 to 25 percent higher than previous algorithms for classification experiments.

A Three-Step Preprocessing Algorithm for Enhanced Classification of E-Mail Recommendation System (이메일 추천 시스템의 분류 향상을 위한 3단계 전처리 알고리즘)

  • Jeong Ok-Ran;Cho Dong-Sub
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.4
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    • pp.251-258
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    • 2005
  • Automatic document classification may differ significantly according to the characteristics of documents that are subject to classification, as well as classifier's performance. This research identifies e-mail document's characteristics to apply a three-step preprocessing algorithm that can minimize e-mail document's atypical characteristics. In the first 5go, uncertain based sampling algorithm that used Mean Absolute Deviation(MAD), is used to address the question of selection learning document for the rule generation at the time of classification. In the subsequent stage, Weighted vlaue assigning method by attribute is applied to increase the discriminating capability of the terms that appear on the title on the e-mail document characteristic level. in the third and last stage, accuracy level during classification by each category is increased by using Naive Bayesian Presumptive Algorithm's Dynamic Threshold. And, we implemented an E-Mail Recommendtion System using a three-step preprocessing algorithm the enable users for direct and optimal classification with the recommendation of the applicable category when a mail arrives.

Seabed Sediment Classification Algorithm using Continuous Wavelet Transform

  • Lee, Kibae;Bae, Jinho;Lee, Chong Hyun;Kim, Juho;Lee, Jaeil;Cho, Jung Hong
    • Journal of Advanced Research in Ocean Engineering
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    • v.2 no.4
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    • pp.202-208
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    • 2016
  • In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

Hardware Accelerated Design on Bag of Words Classification Algorithm

  • Lee, Chang-yong;Lee, Ji-yong;Lee, Yong-hwan
    • Journal of Platform Technology
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    • v.6 no.4
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    • pp.26-33
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    • 2018
  • In this paper, we propose an image retrieval algorithm for real-time processing and design it as hardware. The proposed method is based on the classification of BoWs(Bag of Words) algorithm and proposes an image search algorithm using bit stream. K-fold cross validation is used for the verification of the algorithm. Data is classified into seven classes, each class has seven images and a total of 49 images are tested. The test has two kinds of accuracy measurement and speed measurement. The accuracy of the image classification was 86.2% for the BoWs algorithm and 83.7% the proposed hardware-accelerated software implementation algorithm, and the BoWs algorithm was 2.5% higher. The image retrieval processing speed of BoWs is 7.89s and our algorithm is 1.55s. Our algorithm is 5.09 times faster than BoWs algorithm. The algorithm is largely divided into software and hardware parts. In the software structure, C-language is used. The Scale Invariant Feature Transform algorithm is used to extract feature points that are invariant to size and rotation from the image. Bit streams are generated from the extracted feature point. In the hardware architecture, the proposed image retrieval algorithm is written in Verilog HDL and designed and verified by FPGA and Design Compiler. The generated bit streams are stored, the clustering step is performed, and a searcher image databases or an input image databases are generated and matched. Using the proposed algorithm, we can improve convenience and satisfaction of the user in terms of speed if we search using database matching method which represents each object.

The verification of the hardware implementation of packet classification algorithm on multiple fields by Veriolg-HDL (Verilog-HDL을 이용한 다중필드 패킷분류 알고리듬의 설계 검증)

  • Hong, Seong-Pyo;Kim, Jun-Hyeong;Choe, Won-Ho
    • Proceedings of the KIEE Conference
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    • pp.852-855
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    • 2003
  • This paper reports the RFC(Recursive Flow Classification) algorithm that is available on multiple fields. It is easy to be implemented by both software and hardware. For high speed classification of packets, the implementation of RFC is essential by hardware. Hence, in this paper, RFC algorithm is simulated by Verilog-HDL, and it verify the efficiency of the algorithm. The result shows that the algorithm can perform a packet classification within several cycles. It is not only much faster than software implementation but also enough to support OC192c.

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