Earticle

말라위는 기후변화를 경제･사회적 안정성을 위협하는 핵심 요인으로 인식하고 있으나, 공여자 지원 프로젝트의 기후 관련 여부를 수작업으로 분류하는 과정에서 일관성 부족과 기후재정 추적 오류가 발생하고 있다. 이에 본 연구는 말라위 공여자 지원 프로젝트를 대상으로 기후 관련 프로젝트를 자동 식별하기 위한 XGBoost 기반 이진 분류 모형을 제안한다. 분석에는 말라위 정부 원조관리플랫폼의 지리코딩 및 기후 코딩 데이터를 활용하였으며, 30개 공여기관이 지원한 754개 프로젝트를 대상으로 랜덤 포레스트 및 로지스틱 회귀 모형과 성능을 비교하였다. 분석 결과, 제안된 모형은 매우 높은 분류 정확도를 보였으며, SHAP 분석을 통해 누적 집행액, 공여자 유형, 기후 적응 점수가 핵심 영향 요인으로 확인되었다. 본 연구는 설명 가능한 인공지능을 활용하여 기후 프로젝트 분류의 정확성과 정책 활용 가능성을 제고하였다는 점에서 의의가 있다.

Climate change poses a serious challenge to Malawi’s economic and social stability, highlighting the need for accurate identification of climate-related donor-funded projects. However, existing manual classification approaches are inconsistent and prone to error. To address this issue, this study develops an automated binary classification framework using Extreme Gradient Boosting (XGBoost). The analysis is based on geocoded activity-level and climate-coded data and AidData from Malawi’s government aid management platform, covering 754 projects funded by 30 donors. Model performance is evaluated against Random Forest and Logistic Regression, with SHapley Additive exPlanations (SHAP) applied for feature selection. The results show that the XGBoost model achieves near-perfect accuracy, with cumulative disbursement, donor type, and climate adaptation scores identified as key determinants. These findings demonstrate the effectiveness of explainable AI in improving climate project classification and climate finance management.

Handling imbalanced datasets in binary classification, especially in employment big data, is challenging. Traditional methods like oversampling and undersampling have limitations. This paper integrates TabNet and Generative Adversarial Networks (GANs) to address class imbalance. The generator creates synthetic samples for the minority class, and the discriminator, using TabNet, ensures authenticity. Evaluations on benchmark datasets show significant improvements in accuracy, precision, recall, and F1-score for the minority class, outperforming traditional methods. This integration offers a robust solution for imbalanced datasets in employment big data, leading to fairer and more effective predictive models.

본 연구는 HAM10000 피부 병변 이미지 데이터셋을 활용하여 인공지능 기반의 합성곱 신경망(CNN) 기반의 EfficientNetB0 모델이 피부암을 악성(Malignant)과 양성(Benign)으로 이진 분류하는 성능을 검증하고, 이를 성형외과 전문의 4인의 육안 판독 결 과와 탐색적으로 비교하는 데 목적이 있다. 데이터 전처리 단계에서 PIL 라이브러리를 활용한 이미지 무결성 검증을 수행하여 손상된 이미지 1장을 제거하였으며, 최종 10,014장의 이미지를 사용하였다. 클래스 불균형 해소를 위해 학습 데이터에서만 50:50 균형 샘플링을 적용하였고, ImageNet 사전학습 가중치를 활용한 전이학습(Transfer Learning) 및 전체 레이어 미세조정(Fine-tuning) 방식으로 10-Fold 교차검증을 수행하였다. 실험 결과, EfficientNetB0 모델은 HAM10000 전체 10,014장 기준 10-Fold 교차검증에서 평균 정확도 77.95%(최고 82.48%, 최저 74.09%, 표준편차 2.36%), 민감도 54.68%, 특이도 82.46%, F1-Score 44.59%, AUC-ROC 0.7777을 달성하였다. 또한 비교의 동등성을 확보하기 위해 성형외과 전문의 4인이 판독한 동일 40장에 대해 10-Fold 모델 앙상블 추론을 수행한 결과, AI 모델은 정확도 82.50%, 민감도 95.00%를 기록하여 전문의 평균(정확도 63.10%) 대비 19.40%p 높은 성능을 보였다. 본 연구는 AI 기반 피부암 스크리닝 시스템이 비전문 의료 환경의 일반 건강검진 프로그램에서 보조 도구로 활용될 수 있는 가능성을 탐색적으로 제시한다.

This study aims to verify the performance of an AI-based Convolutional Neural Network (CNN)-based EfficientNetB0 model in binary classification of skin cancer into malignant and benign categories using the HAM10000 skin lesion image dataset, and to exploratorily compare its diagnostic accuracy with the visual reading results of four plastic surgeons. During data preprocessing, image integrity verification was performed using the PIL library, removing one corrupted image, and a final dataset of 10,014 images was used. To address class imbalance, 50:50 balanced sampling was applied only to the training data, and the model was trained using transfer learning with ImageNet pre-trained weights and full-layer fine-tuning with 10-Fold cross-validation. The experimental results show that the EfficientNetB0 model achieved a mean accuracy of 77.95% (maximum 82.48%, minimum 74.09%, standard deviation 2.36%), with sensitivity 54.68%, specificity 82.46%, F1-Score 44.59%, and AUC-ROC 0.7777 on the full HAM10000 dataset of 10,014 images via 10-Fold cross-validation. Furthermore, to ensure comparability, the AI model was additionally evaluated on the same 40 images reviewed by four plastic surgeons using 10-Fold ensemble inference, achieving an accuracy of 82.50% and sensitivity of 95.00%, which is 19.40%p higher than the mean accuracy of four plastic surgeons (63.10%). This study exploratorily demonstrates the potential of AI-based skin cancer screening systems to be utilized as auxiliary tools in general health checkup programs in non-specialist medical environments.

본 연구는 교과서 콘텐츠의 하이라이팅 대상인 ‘후보 핵심 용어’를 식별하기 위한 ‘텍스트 하이라이팅 모델(Text Highlighting Model)’ 관련 선행연구를 확장한 연구이 다. 개선된 ‘핵심 용어 추출 모델’은 1~2어절의 ’후보 핵심 용어‘를 교과목의 ’핵심 용 어‘로 자동 분류하여, 학습자에게 미리 주요 ’핵심 용어‘를 제시하고, 강조하는 방안을 제안한다.

Onion Router라고 알려진 Tor는 강한 익명성을 보장하기 때문에 각종 범죄행위뿐만 아니라 신속한 포트 검색 및 인 증정보의 외부 유출 등 해킹 시도에도 활발하게 이용되고 있다. 따라서 범죄 시도를 조기에 차단하고 해킹으로부터 조 직의 정보시스템을 안전하게 보호하기 위해서는 Tor 트래픽의 빠르고 정확한 탐지가 상당히 중요하다. 이에 본 논문에 서는 CNN(Convolutional Neural Network)을 기반으로 Tor 트래픽을 탐지하고 트래픽의 유형을 분류하는 분류모델을 제 안한다. 제안하는 분류모델의 성능 검증에는 UNB Tor 2016 데이터세트가 사용되었다. 실험을 진행한 결과, 제안하는 접 근방법은 Tor 및 Non-Tor 트패픽을 탐지하는 이진분류에서는 99.98%, Tor 트래픽의 유형을 구분하는 다중분류에서는 97.27%의 정확도를 보여주었다.

Tor, known as Onion Router, guarantees strong anonymity. For this reason, Tor is actively used not only for criminal activities but also for hacking attempts such as rapid port scan and the ex-filtration of stolen credentials. Therefore, fast and accurate detection of Tor traffic is critical to prevent the crime attempts in advance and secure the organization's information system. This paper proposes a novel classification model that can detect Tor traffic and classify the traffic types based on CNN(Convolutional Neural Network). We use UNB Tor 2016 Dataset to evaluate the performance of our model. The experimental results show that the accuracy is 99.98% and 97.27% in binary classification and multiclass classification respectively.

Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper proposes a new feature selection using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights. The proposed method was validated using five tweet datasets on different topics both in Indonesian and English, and compared with state-of-the-art of filter and wrapper-based feature selection methods. Experimental results show the proposed method significantly improves sentiment classification performance and decrease computational time.

최근 학습중인 언어를 사용하여 구체적으로 무엇을 할 수 있는지를 나타내는 범용 체계에 큰 관심이 모아지고　있다. 그 중에서도 2001년에 유럽위원회가 발표한 Common European Framework of Reference for Languages (CEFR)는 언어능력의 국제표 준으로 세계적으로 평가가 높다. 2017년에는 그것을 보완하는 CEFR Companion Volume이 공개되어, PreA1 레벨이 추가되는 등 더욱 더 레벨이 세분화되었다. CEFR 를 사용한 연구와 실천 예는 영어를 비롯한 많은 언어에서 이루어지고 있는 반면, 일 본어 교육을 염두에 둔 CEFR연구는 수적으로도 여전히 적으며, 일본어 CEFR준수 텍 스트 코퍼스도 현재까지의 연구 결과 존재하지 않는다. 본 연구에서는, 코퍼스를 작성 할 때 발생하는, 예문에 CEFR의 독해력을 반영하는 Can-Do Statements (CDS)를 부여 하는 노력을 경감하기 위해 자동분류 실장(実装)에 대해 지속적으로 연구하고 있다. 분류 방법에는 Support Vector Machine과 랜덤 포레스트에 의한 지도 학습을 적용하 고, 기계 학습을 위한 예문의 특징량으로써 문서 유형, 전문성, 문장 길이, 한자 비율 4 개를 사용한다.　Pre-A1 레벨은 종전 레벨 군과 난이도와 구성언어요소에 큰 차이가 있기 때문에, 모든 레벨의 CDS를 한 번에 분류하는 과거 방식에 비해 2 단계에 따른 CDS 분류에 따른 정확도 향상을 목표로 하였다. 또한, Web 어플리케이션의 개발을 실시하여, 자동 분류 알고리즘을 내부 구현함으로써, 주어진 예문에 대하여 이에 대응 하는CDS를 자동 부여하는 기능과, 특정 CDS를 선택함으로써 이에 해당하는 예문 리 스트를 그 확실성 순으로 제공하는 기능을 제공하고 있다.

In recent years, a lot of attention is being paid to general-purpose frameworks that show what can be concretely done in using a target language for learners. In 2017, the Common European Framework of Reference for Languages (CEFR) Companion Volume was released. This volume complements the CEFR initially published in 2001, which is widely considered as an international standard for language ability, and introduces a Pre-A1 level. Conversely, there are few studies on CEFR for Japanese language education, and from the past studies, it was noted that there are no Japanese CEFR compliant text corpora. Thus, the present study aims to classify example sentences according to their corresponding Can-Do Statements (CDSs) to reduce efforts in creating a corpus. Support Vector Machine and Random Forest were applied to the classification approach where document types, specialty, sentence length, and kanji ratio have been given as the features of example sentences. The Pre-A1 level has a great difference in difficulty level and constituent language elements from the previous level groups. Therefore, our study seeks to improve the accuracy through binary classification combined with incorporation of the past method of classifying all levels of CDSs at once. Moreover, we also developed a web application that would help attach CDSs efficiently to example sentences and provide example sentence collections corresponding to specific CDSs.

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

본 연구는 유전 알고리듬에 기반한 새로운 도산예측기법을 개발하고 그 기법의 타당성 및 예측 우수성을 검증하는데 목적이 있다. 본 연구에서 제안하는 이진분류기법은 도산기업과 비도산기업을 대표할 수 있는 가상기업(virtual company)을 설정하고, 그 가상기업과 분류대상 기업 간의 유사도를 측정하여 도산여부를 분류하는 방법론으로, 가상기업의 변수 값과 각 변수의 가중치는 훈련용 자료의 분류정확도를 극대화할 수 있도록 유전 알고리듬을 이용하여 구하게 된다. 본 연구에서 제안하는 기법의 타당성을 검증하기 위해 기존의 도산예측기법과 예측성과를 실험을 통해 비교한 결과, 본 연구에서 개발한 기법의 예측력이 기존의 다변량판별분석, 로지스틱 회귀모형, 의사결정나무, 인공신경망 모형보다 높은 수준을 보이는 것을 확인하였다.

낙상사고는 많은 재활 비용을 필요로 하고, 다른 안전사고에 비해 대부분 예방이 가능하다는 점에서 올 바른 정보제공을 통한 사전 예방이 더욱 중요하다. 특히, 고령자의 경우 연령증가에 따른 신체의 노화 및 질병은 안전사고의 증가로 이어질 수 있고 상해의 정도도 심각한 실정이다. 따라서, 본 논문에서는 환자에 부착된 통합 센서 모듈에서 실시간으로 수집되는 데이터를 기반으로 낙상 사고에 대한 예측 데이터 모델링을 통해 실시간으로 낙상에 대한 모니터링이 가능한 데이터 분석을 연구하였다. 이를 위해 온도 센서, 심박 센서 및 모션 센서에서 실 시간으로 데이터를 수집하고 이항 분류 모델링을 적용한 낙상 예측 데이터 분석을 수행하였다.

Fall accidents require a lot of rehabilitation costs. Since most of the accidents can be prevented compared to other safety accidents, it is more important to prevent them in advance by providing correct information. In particular, in the case of the elderly, body aging and disease due to an increase in age can lead to an increase in safety accidents, and the degree of injury is serious. Therefore, in this paper, we designed predictive data modeling for a fall accident based on the data collected in real time from the integrated sensor module attached to the patient. In addition, data analysis that can monitor falls in real time was studied. To this end, data was collected in real time from temperature sensors, heart rate sensors, and motion sensors, and fall prediction data analysis was performed by applying binomial classification modeling.

전 세계적으로 유행하는 코로나19로 인해 많은 사망자가 보고되고 있다. 코로나19의 추가 확산을 막기 위해서 는 의심 환자에 대해 신속하고 정확한 영상판독을 한 후, 적절한 조치를 취해야 한다. 이를 위해 본 논문은 환자의 감염 여부를 의료진에게 제공해 영상판독을 보조할 수 있는 딥 러닝 기반 코로나19 흉부 X선 판독 기법을 소개한다. 우선 판독모델을 학습하기 위해서는 충분한 데이터셋이 확보되어야 하는데, 현재 제공하는 코로나19 오픈 데이터셋은 학습의 정확도를 보장하기에 그 영상 데이터 수가 충분하지 않다. 따라서 누적 적대적 생성 신경망(StackGAN++)을 사용해 인공지능 학습 성능을 저하하는 영상 데이터 수적 불균형 문제를 해결하였다. 다음으로 판독모델 개발을 위해 증강된 데이터셋을 사용하여 DenseNet 기반 분류모델 학습을 진행하였다. 해당 분류모델은 정상 흉부 X선과 코로나 19 흉부 X선 영상을 이진 분류하는 모델로, 실제 영상 데이터 일부를 테스트데이터로 사용하여 모델의 성능을 평가 하였다. 마지막으로 설명 가능한 인공지능(eXplainable AI, XAI) 중 하나인 Grad-CAM을 사용해 입력 영상의 질환 유무를 판단하는 근거를 제시하여 모델의 신뢰성을 확보하였다.

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

In order to reduce the data storage and improve data compression ratio of the stiffness matrix of 3D finite element, after analyzed the relationship between nonzero submatrix and generalized adjacent nodes of the stiffness matrix, this paper proposes an improved stiffness matrix compression algorithm, which combined negative sign compressed sparse line and a rider to store binary classification method. Then the improved algorithm is applied to the storage of the stiffness matrix of 3D-FEM. Through experimental simulation, the results show that this method saves a lot of storage space to ensure the validity of data for finite element analysis.

A face recognition algorithm based on local binary Haar feathers which represented as Kadane optimizing multi-threshold AdaBoost was proposed according to the problems of texture shape feature representation and classification algorithm accuracy in the process of facial classifying detection and recognition, First, improve the traditional expression by using image Local binary pattern of Haar features , improve image model of texture and shape feature expression ability ; Secondly, for single threshold weak learning algorithm we can not make full use of local binary Haar feature information, resulting in a lower classification accuracy problem proposed Kadane optimizing multi-threshold AdaBoost classifier, to achieve local binary Haar feature representation of facial high accuracy recognition; Finally, through the experiments show, efficient face recognition rate can reach more than 90% by the algorithm,which is superior to the selected comparison algorithm.

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one- hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.

We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.

각종 감시체계에서 육안에 의존하여 물체를 식별해내는 것은 어렵고 실수하기 쉬우므로 군 감시체계에서 자동식별능력의 필요성은 더욱 높아지고 있다. 사회에 발표되는 모형들은 군 무기체계에 대한 데이터가 반영되지 않아 군에 바로 적용하는 것은 제한된다. 본 연구는 군용 헬기의 이미지에 합성곱 신경망을 적용하여 피아식별 모형을 구축한 연구이다. 제안하는 모형은 우리나라에서 주로 사용하고 있는 헬기인 AH-64 기종과 공산권 국가에서 주로 사용하고 있는 헬기인 Mi-17 기종의 이미지를 통해 학습시켜 구축되었다. 제안하는 모형의 성능을 살펴보면, 평가척도를 이용하여 평가한 결과 97.8%의 정확도, 97.3%의 정밀도, 98.5% 재현율과 97.9%의 F-measure의 성능을 보임을 확인하였다. 이런 분류 결과에 대해서 Feature-map을 통해 아군 헬기의 바퀴와 무장, 그리고 흡기구 주변이, 적군 헬기의 바퀴, 흡기구, 그리고 창문 부위가 피아식별 모형의 분류 기준임을 확인할 수 있었다. 본 연구는 CNN을 이용하여 군 무기체계 중 헬기의 영상정보에 대한 피아식별에 대한 분류를 처음으로 시도한 연구이며, 본 연구에서 제안하는 모형은 기존의 다른 무기체계에 대한 분류 모형보다 높은 정확도를 보인다.

There has been difficulties in identifying objects by relying on the naked eye in various surveillance systems. There is a growing need for automated surveillance systems to replace soldiers in the field of military surveillance operations. Even though the object detection technology is developing rapidly in the civilian domain, but the research applied to the military is insufficient due to a lack of data and interest. Thus, in this paper, we applied one of deep learning algorithms, Convolutional Neural Network-based binary classification to develop an autonomous identification model of both friend and foe helicopters (AH-64, Mi-17) among the military weapon systems, and evaluated the model performance by considering accuracy, precision, recall and F-measure. As the result, the identification model demonstrates 97.8%, 97.3%, 98.5%, and 97.8 for accuracy, precision, recall and F-measure, respectively. In addition, we analyzed the feature map on convolution layers of the identification model in order to check which area of imagery is highly weighted. In general, rotary shaft of rotating wing, wheels, and air-intake on both of ally and foe helicopters played a major role in the performance of the identification model. This is the first study to attempt to classify images of helicopters among military weapons systems using CNN, and the model proposed in this study shows higher accuracy than the existing classification model for other weapons systems.

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.