• Journal of Korean Library and Information Science Society
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• v.32 no.3
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• pp.307-330
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• 2001

This study experimented the performance of categorization methods using the kNN classifier. Most sample based automatic text categorization techniques like the kNN classifier reduces the feature set of the training documents. We sought to find out which percentage reductions in the feature set would result in high performances. In addition, the kNN classifier has to find the k number of training documents most similar to the test documents in the training documents. We sought to verify the most appropriate k value through experiments.

• Proceedings of the Korean Society for Information Management Conference
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• 2003.08a
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• pp.65-72
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• 2003

kNN 기법은 높은 자동분류 성능을 보여주지만 처리 속도가 느리다는 단점이 있다. 이를 극복하기 위해 입력문서의 대표용어 w개를 선정하고 이를 포함한 학습문서만으로 학습집단을 축소함으로써 자동분류 속도를 향상시키는 kw_kNN을 제안하였다. 실험 결과 대표 용어를 5개 사용할 경우에는 kNN 대비 문서간 비교횟수를 평균 18.4%로 축소할 수 있었다. 그러면서도 성능저하를 최소화하여 매크로 평균 F1 척도면에서는 차이가 없고 마이크로 평균정확률 면에서는 약 l∼2% 포인트 이내로 kNN 기법의 성능에 근접한 결과를 얻었다.

• The Journal of Information Technology
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• v.1 no.1
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• pp.65-84
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• 1998

The MBR (Memory Based Reasoning) is a supervised learning method that utilizes the distances among the input and trained patterns in its classification, and is also called a distance based learning algorithm. The MBR is based on the k-NN classifier, in which teaming is performed by simply storing training patterns in the memory without any further processing. This paper proposes a new learning algorithm which is more efficient than the traditional k-NN classifier and has incremental learning capability, Furthermore, our proposed algorithm is insensitive to noisy patterns, and guarantees more efficient memory usage.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1204-1217
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• 2004

Numerous statistical and machine learning techniques have been studied for automatic text classification. However, because they train the classifiers using only feature vectors of documents, ambiguity between two possible categories significantly degrades precision of classification. To remedy the drawback, we propose a new method which incorporates relationship information of categories into extant classifiers. In this paper, we first perform the document classification using the k-NN classifier which is generally known for relatively good performance in spite of its simplicity. We employ the relationship information from an object-based thesaurus to reduce the ambiguity. By referencing various relationships in the thesaurus corresponding to the structured categories, the precision of k-NN classification is drastically improved, removing the ambiguity. Experiment result shows that this method achieves the precision up to 13.86% over the k-NN classification, preserving its recall.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.849-854
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• 2004

This paper proposes an algorithm to enhance the execution time of kNN in the document classification. The proposed algorithm is to enhance the execution time by minimizing the computing cost of the similarity between two documents by using the list of pairs, while the conventional kNN uses the iist of pairs. The 1ist of pairs can be obtained by applying the matrix transposition to the list of pairs at the training phase of the document classification. This paper analyzed the proposed algorithm in the time complexity and compared it with the conventional kNN. And it compared the proposed algorithm with the conventional kNN by using routers-21578 data experimentally. The experimental results show that the proposed algorithm outperforms kNN about $90{\%}$ in terms of the ex-ecution time.

• Journal of Korean Library and Information Science Society
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• v.44 no.1
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• pp.27-47
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• 2013

This study investigated the classification performance of a kNN classifier using the feature selection methods based on document frequency(DF) and collection frequency(CF). The results of the experiments, which used HKIB-20000 data, were as follows. First, the feature selection methods that used high-frequency terms and removed low-frequency terms by the CF criterion achieved better classification performance than those using the DF criterion. Second, neither DF nor CF methods performed well when low-frequency terms were selected first in the feature selection process. Last, combining CF and DF criteria did not result in better classification performance than using the single feature selection criterion of DF or CF.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.2
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• pp.123-146
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• 2005

This study aims to find consistent strategies for feature selection and feature weighting methods, which can improve the effectiveness and efficiency of kNN text classifier. Feature selection criteria and feature weighting methods are as important factor as classification algorithms to achieve good performance of text categorization systems. Most of the former studies chose conflicting strategies for feature selection criteria and weighting methods. In this study, the performance of several feature selection criteria are measured considering the storage space for inverted index records and the classification time. The classification experiments in this study are conducted to examine the performance of IDF as feature selection criteria and the performance of conventional feature selection criteria, e.g. mutual information, as feature weighting methods. The results of these experiments suggest that using those measures which prefer low-frequency features as feature selection criterion and also as feature weighting method. we can increase the classification speed up to three or five times without loosing classification accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.871-875
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• 2008

This paper deals with the feature selection for multiple k-nearest neighbor (k-NN) classifiers using Genetic Algorithm with Varying reputation Size (GAVaPS). Because we use multiple k-NN classifiers, the feature selection problem for them is vary hard and has large search region. To solve this problem, we employ the GAVaPS which outperforms comparison with simple genetic algorithm (SGA). Further, we propose the efficient combining method for multiple k-NN classifiers using GAVaPS. Experiments are performed to demonstrate the efficiency of the proposed method.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1849-1857
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• 1999

We proposed the RPA (Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. This algorithm recursively partitions the pattern space until each hyperrectangle contains only those patterns of the same class, then it computes the average values of patterns in each hyperrectangle to extract a representative. Also we have used the mutual information between the features and classes as weights for features to improve the classification performance. The proposed algorithm used 30~90% of memory space that is needed in the k-NN (k-Nearest Neighbors) classifier, and showed a comparable classification performance to the k-NN. Also, by reducing the number of stored patterns, it showed an excellent result in terms of classification time when we compare it to the k-NN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.956-961
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• 2006

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user.