• Title, Summary, Keyword: 데이터 불균형

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EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems (데이터 불균형 해결을 위한 Under-Sampling 기반 앙상블 SVMs)

  • Gang Pil-Seong;Jo Seong-Jun
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.291-298
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    • 2006
  • 패턴인식 문제에서 한 범주에 속한 데이터의 수가 다른 범주에 속한 데이터의 수보다 극히 많거나 적으면 데이터 불균형이 발생했다고 한다. Support Vector Machine(SVM)은 다른 기계 학습 알고리즘들과 마찬가지로 학습에 사용되는 데이터의 범주간 비율이 거의 비슷하다는 가정 하에서 학습을 하고 예측 결과를 도출하게 된다. 그러나 실제 문제에서는 데이터의 불균형이 발생하는 경우가 매우 빈번하며, 이러한 경우에는 모델의 성능이 매우 저하되는 문제점이 발생한다. 본 논문에서는 실제로 데이터 불균형이 SVM의 분류 결과에 어떠한 영향을 미치는지를 2차원 인공 데이터를 통하여 알아본다. 그리고 이러한 데이터 불균형을 해소하기 위하여 Under-Sampling 기반 앙상블 SVM을 제안하였다. 제안된 방법을 두 가지 인공 데이터에 적용하여 본 결과, 제안된 방법은 데이터 불균형을 해소하기 위해 사용되는 기존의 방법들에 비하여 소수 범주에 속하는 데이터의 수가 매우 적고 데이터의 불균형이 매우 심한 경우에도 높은 성능과 안정성을 갖는 효과적인 방법이라는 것이 입증되었다.

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Handling Method of Imbalance Data for Machine Learning : Focused on Sampling (머신러닝을 위한 불균형 데이터 처리 방법 : 샘플링을 위주로)

  • Lee, Kyunam;Lim, Jongtae;Bok, Kyoungsoo;Yoo, Jaesoo
    • The Journal of the Korea Contents Association
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    • v.19 no.11
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    • pp.567-577
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    • 2019
  • Recently, more and more attempts have been made to solve the problems faced by academia and industry through machine learning. Accordingly, various attempts are being made to solve non-general situations through machine learning, such as deviance, fraud detection and disability detection. A variety of attempts have been made to resolve the non-normal situation in which data is distributed disproportionately, generally resulting in errors. In this paper, we propose handling method of imbalance data for machine learning. The proposed method to such problem of an imbalance in data by verifying that the population distribution of major class is well extracted. Performance Evaluations have proven the proposed method to be better than the existing methods.

Learning Behavior Analysis of Bayesian Algorithm Under Class Imbalance Problems (클래스 불균형 문제에서 베이지안 알고리즘의 학습 행위 분석)

  • Hwang, Doo-Sung
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.45 no.6
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    • pp.179-186
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    • 2008
  • In this paper we analyse the effects of Bayesian algorithm in teaming class imbalance problems and compare the performance evaluation methods. The teaming performance of the Bayesian algorithm is evaluated over the class imbalance problems generated by priori data distribution, imbalance data rate and discrimination complexity. The experimental results are calculated by the AUC(Area Under the Curve) values of both ROC(Receiver Operator Characteristic) and PR(Precision-Recall) evaluation measures and compared according to imbalance data rate and discrimination complexity. In comparison and analysis, the Bayesian algorithm suffers from the imbalance rate, as the same result in the reported researches, and the data overlapping caused by discrimination complexity is the another factor that hampers the learning performance. As the discrimination complexity and class imbalance rate of the problems increase, the learning performance of the AUC of a PR measure is much more variant than that of the AUC of a ROC measure. But the performances of both measures are similar with the low discrimination complexity and class imbalance rate of the problems. The experimental results show 4hat the AUC of a PR measure is more proper in evaluating the learning of class imbalance problem and furthermore gets the benefit in designing the optimal learning model considering a misclassification cost.

SVM Ensemble Techniques for Class Imbalance Problem (데이터 불균형 문제에서의 SVM 앙상블 기법의 적용)

  • 강필성;이형주;조성준
    • Proceedings of the Korean Information Science Society Conference
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    • pp.706-708
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    • 2004
  • 대부분의 기계학습 알고리즘은 학습 데이터에서 각각의 범주간의 비율이 동일하거나 비슷하다는 가정 하에 문제를 풀게 된다. 그러나 실제 문제에서는 그 비율이 동일하지 않으며 매우 큰 차이를 보이기도 하는데, 이는 분류 성능을 저하시키는 요인이기도 하다 따라서 본 논문에서는 이러한 데이터의 불균형 문제를 해소하는 방안으로 SVM 앙상블 기법을 적용한 샘플링을 제안하고 이를 실제 불균형 데이터에 적용함으로써 제안된 방법이 기존의 방법들에 비해 향상된 성능을 나타내는 것을 보였다.

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Combined Application of Data Imbalance Reduction Techniques Using Genetic Algorithm (유전자 알고리즘을 활용한 데이터 불균형 해소 기법의 조합적 활용)

  • Jang, Young-Sik;Kim, Jong-Woo;Hur, Joon
    • Journal of Intelligence and Information Systems
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    • v.14 no.3
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    • pp.133-154
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    • 2008
  • The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. In order to solve the data imbalance problem, there has been proposed a number of techniques based on re-sampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

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Classification of Imbalanced Data Using Multilayer Perceptrons (다층퍼셉트론에 의한 불균현 데이터의 학습 방법)

  • Oh, Sang-Hoon
    • The Journal of the Korea Contents Association
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    • v.9 no.7
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    • pp.141-148
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    • 2009
  • Recently there have been many research efforts focused on imbalanced data classification problems, since they are pervasive but hard to be solved. Approaches to the imbalanced data problems can be categorized into data level approach using re-sampling, algorithmic level one using cost functions, and ensembles of basic classifiers for performance improvement. As an algorithmic level approach, this paper proposes to use multilayer perceptrons with higher-order error functions. The error functions intensify the training of minority class patterns and weaken the training of majority class patterns. Mammography and thyroid data-sets are used to verify the superiority of the proposed method over the other methods such as mean-squared error, two-phase, and threshold moving methods.

Machine Learning Based Intrusion Detection Systems for Class Imbalanced Datasets (클래스 불균형 데이터에 적합한 기계 학습 기반 침입 탐지 시스템)

  • Cheong, Yun-Gyung;Park, Kinam;Kim, Hyunjoo;Kim, Jonghyun;Hyun, Sangwon
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.27 no.6
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    • pp.1385-1395
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    • 2017
  • This paper aims to develop an IDS (Intrusion Detection System) that takes into account class imbalanced datasets. For this, we first built a set of training data sets from the Kyoto 2006+ dataset in which the amounts of normal data and abnormal (intrusion) data are not balanced. Then, we have run a number of tests to evaluate the effectiveness of machine learning techniques for detecting intrusions. Our evaluation results demonstrated that the Random Forest algorithm achieved the best performances.

An Study on Decision Tree Analysis with Imbalanced Data Set : A Case of Health Insurance Bill Audit in General Hospital (의사결정나무 분석에서 불균형 자료의 분석 연구 : 종합병원의 건강보험료 청구 심사 사례)

  • Heo Jun;Kim Jong-U
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.1667-1676
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    • 2006
  • 다른 산업과 달리 병원/의료 산업에서는 건강 보험료 심사 평가라는 독특한 검증 과정이 필수적으로 있게 된다. 건강 보험료 심사 평가는 병원의 수익 문제 뿐 아니라 적정한 진료행위를 하는 병원이라는 이미지와도 맞물려 매우 중요한 분야이며, 특히 대형 종합병원일수록 이 부분에 많은 심사관련 인력들을 투입하여, 병원의 수익과 명예를 위해서 업무를 수행하고 있다. 본 논문은 이러한 건강보험료 청구 심사 과정에서, 사전에 수많은 진료 청구 건 중 심사 평가에서 삭감이 될 수 있는 진료 청구 건을 데이터 마이닝을 통해서 발견하여, 사전의 대비를 철저히 하고자 하는 한 국내의 대형 종합병원의 사례를 소개하고자 한다. 데이터 마이닝을 적용함에 있어, 주요한 문제점 중의 하나는 바로 지도학습 기법을 적용하기에 곤란한 데이터 불균형 문제가 발생하는 것이다. 이런 불균형 문제를 해소하고, 비교 조건 중에 가장 효율적인 삭감 예상 진료 건 탐지 모형을 만들어 내기 위하여 데이터 불균형 문제의 기본 해법인 과, Sampling 오분류 비용의 다양하고 혼합적인 적용을 통하여, 적합한 조건을 가지는 의사결정 나무 모형을 도출하였다.

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Improved Focused Sampling for Class Imbalance Problem (클래스 불균형 문제를 해결하기 위한 개선된 집중 샘플링)

  • Kim, Man-Sun;Yang, Hyung-Jeong;Kim, Soo-Hyung;Cheah, Wooi Ping
    • The KIPS Transactions:PartB
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    • v.14B no.4
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    • pp.287-294
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    • 2007
  • Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

Fault Detection of Unbalanced Cycle Signal Data Using SOM-based Feature Signal Extraction Method (SOM기반 특징 신호 추출 기법을 이용한 불균형 주기 신호의 이상 탐지)

  • Kim, Song-Ee;Kang, Ji-Hoon;Park, Jong-Hyuck;Kim, Sung-Shick;Baek, Jun-Geol
    • Journal of the Korea Society for Simulation
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    • v.21 no.2
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    • pp.79-90
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    • 2012
  • In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling's $T^2$ Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.