• Journal of radiological science and technology
• /
• v.42 no.1
• /
• pp.9-17
• /
• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.15 no.3
• /
• pp.145-152
• /
• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

• Proceedings of the Korea Database Society Conference
• /
• 2001.06a
• /
• pp.331-341
• /
• 2001

본 논문에서는 Naive Bayes 문서 분류기를 위한 새로운 학습모델을 제안한다. 이 모델에서는 라벨이 없는 문서들의 집합으로부터 선택한 적은 수의 학습 문서들을 이용하여 문서 분류기를 재학습한다. 본 논문에서는 이러한 학습 방법을 따를 경우 작은 비용으로도 문서 분류기의 정확도가 크게 향상될 수 있다는 사실을 보인다. 이와 같이, 알고리즘을 통해 라벨이 없는 문서들의 집합으로부터 정보량이 큰 문서를 선택한 후, 전문가가 이 문서에 라벨을 부여하는 방식으로 학습문서를 결정하는 것을 selective sampling이라 한다. 본 논문에서는 이러한 selective sampling 문제를 Naive Bayes 문서 분류기에 적용한다. 제안한 학습 방법에서는 라벨이 없는 문서들의 집합으로부터 재학습 문서를 선택하는 기준 측정치로서 평균절대편차(Mean Absolute Deviation), 엔트로피 측정치를 사용한다. 실험을 통해서 제안한 학습 방법이 기존의 방법인 신뢰도(Confidence measure)를 이용한 학습 방법보다 Naive Bayes 문서 분류기의 성능을 더 많이 향상시킨다는 사실을 보인다.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.2
• /
• pp.417-426
• /
• 2024

In general, the implementation of machine learning requires prior knowledge and experience with deep learning models, and substantial computational resources and time are necessary for data processing. As a result, machine learning encounters several limitations when deployed on embedded processors. To address these challenges, this paper introduces a novel approach where a genetic algorithm is applied to the convolution operation within the machine learning process, specifically for performing a selective convolution operation.In the selective convolution operation, the convolution is executed exclusively on pixels identified by a genetic algorithm. This method selects and computes pixels based on a ratio determined by the genetic algorithm, effectively reducing the computational workload by the specified ratio. The paper thoroughly explores the integration of genetic algorithms into machine learning computations, monitoring the fitness of each generation to ascertain if it reaches the target value. This approach is then compared with the computational requirements of existing methods.The learning process involves iteratively training generations to ensure that the fitness adequately converges.

• The Journal of Information Technology and Database
• /
• v.8 no.1
• /
• pp.95-104
• /
• 2001

In the text classification domain, labeling the training documents is an expensive process because it requires human expertise and is a tedious, time-consuming task. Therefore, it is important to reduce the manual labeling of training documents while improving the text classifier. Selective sampling, a form of active learning, reduces the number of training documents that needs to be labeled by examining the unlabeled documents and selecting the most informative ones for manual labeling. We apply this methodology to Naive Bayes, a text classifier renowned as a successful method in text classification. One of the most important issues in selective sampling is to determine the criterion when selecting the training documents from the large pool of unlabeled documents. In this paper, we propose two measures that would determine this criterion : the Mean Absolute Deviation (MAD) and the entropy measure. The experimental results, using Renters 21578 corpus, show that this proposed learning method improves Naive Bayes text classifier more than the existing ones.

• Journal of the Korean institute of surface engineering
• /
• v.55 no.3
• /
• pp.156-163
• /
• 2022

An ammonium ion with a size and charge similar to that of potassium can bind to valinomycin, which is used as an ion carrier for potassium, and cause a meaningful interference effect on the detection of potassium ions. Currently, there are few ion sensors that correct the interference effect of ammonium ions, and there are few studies that specifically suggest the mechanism of the interference effect. By fabricating a SPCE-based potassium ion-selective electrode, the electromotive force was measured in the concentration range of potassium in the nutrient solution, and the linear range was measured to be 10^-5 to 10^-2 M, and the detection limit was 10^-5.19 M. And the interference phenomenon of the potassium sensor was investigated in the concentration range of ammonium ions present in the nutrient solution. Therefore, a data-based analysis strategy using deep learning was presented as a method to minimize the interference effect.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.9
• /
• pp.349-358
• /
• 2021

Recently, a method of finding the attention area or localization area for an object of an image using CAM (Class Activation Map)[1] has been variously carried out as a study of WSOL (Weakly Supervised Object Localization). The attention area extraction from the object heat map using CAM has a disadvantage in that it cannot find the entire area of the object by focusing mainly on the part where the features are most concentrated in the object. To improve this, using CAM and Selective Search[6] together, we first expand the attention area in the heat map, and a Gaussian smoothing is applied to the extended area to generate retraining data. Finally we train the data to expand the attention area of the objects. The proposed method requires retraining only once, and the search time to find an localization area is greatly reduced since the selective search is not needed in this stage. Through the experiment, the attention area was expanded from the existing CAM heat maps, and in the calculation of IOU (Intersection of Union) with the ground truth for the bounding box of the expanded attention area, about 58% was improved compared to the existing CAM.

• Journal of KIISE:Software and Applications
• /
• v.29 no.9
• /
• pp.654-668
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2527-2545
• /
• 2016

Measuring the similarity of given samples is a key problem of recognition, clustering, retrieval and related applications. A number of works, e.g. kernel method and metric learning, have been contributed to this problem. The challenge of similarity learning is to find a similarity robust to intra-class variance and simultaneously selective to inter-class characteristic. We observed that, the similarity measure can be improved if the data distribution and hidden semantic information are exploited in a more sophisticated way. In this paper, we propose a similarity learning approach for retrieval and recognition. The approach, termed as LDA-FEK, derives free energy kernel (FEK) from Latent Dirichlet Allocation (LDA). First, it trains LDA and constructs kernel using the parameters and variables of the trained model. Then, the unknown kernel parameters are learned by a discriminative learning approach. The main contributions of the proposed method are twofold: (1) the method is computationally efficient and scalable since the parameters in kernel are determined in a staged way; (2) the method exploits data distribution and semantic level hidden information by means of LDA. To evaluate the performance of LDA-FEK, we apply it for image retrieval over two data sets and for text categorization on four popular data sets. The results show the competitive performance of our method.

• Korea Journal of Hospital Management
• /
• v.18 no.2
• /
• pp.127-152
• /
• 2013

The purpose of this study was to investigate the effect of balancing, coordinating and learning strategy on performance of private university hospitals. We think that the study will contribute to establish effective management strategy of private university hospitals. Data were collected from 69 private university hospitals. We measured balancing, coordinating and learning strategy, and perceived performance of the hospital by using 5-point Likert scale. Upper-grade general hospitals were significantly higher rate of growth and profitability than others. However, general hospitals were higher level in perceived performance than upper-grade general hospitals. Hospitals located in Seoul were significantly higher growth rate than those in other regions. Large-scale hospitals were significantly higher rate of growth and profitability than small hospitals. Qualitative performance did not different in any hospital characteristics. Growth of hospitals were significantly influenced from business strategies: selective strategy, formal coordinating strategy, and external learning strategy. Profitability of hospitals were also significantly influenced from business strategies: selective strategy, adaptive strategy, and external learning strategy. Subjective performance of hospitals were significantly influenced from external learning strategy. There were no factors that are significantly influencing on qualitative performance of hospital. To have successful performance in the competitive environment, it is recommended that private university hospitals should have to establish management strategy such as balancing, coordinating, and learning strategy.