• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.4
• /
• pp.860-880
• /
• 2024

Breast cancer is a major health concern affecting women and men globally. Early detection and accurate classification of breast cancer are vital for effective treatment and survival of patients. This study addresses the challenge of accurately classifying breast tumors using machine learning classifiers such as MLP, AdaBoostM1, logit Boost, Bayes Net, and the J48 decision tree. The research uses a dataset available publicly on GitHub to assess the classifiers' performance and differentiate between the occurrence and non-occurrence of breast cancer. The study compares the 10-fold and 5-fold cross-validation effectiveness, showing that 10-fold cross-validation provides superior results. Also, it examines the impact of varying split percentages, with a 66% split yielding the best performance. This shows the importance of selecting appropriate validation techniques for machine learning-based breast tumor classification. The results also indicate that the J48 decision tree method is the most accurate classifier, providing valuable insights for developing predictive models for cancer diagnosis and advancing computational medical research.

• The Korean Journal of Ecology
• /
• v.17 no.4
• /
• pp.471-484
• /
• 1994

The forest vegetation types and their structural characteristics in Mt. $Mud\v{u}ng$ were investigated by classification method and ordination method. The forest was classified into 7 communities by ristic composition table: Quercus monogolica community, Q. serrata community, Q.acutissima community, Q.variabilis community, Q.dentata community, Pinus densiflora community and Frainus mandshurica community. Considering the moisture gradient, two kinds of distributuin pattern were shown as follows; F. mandshurica, Q. acturissima, Platycarya strobilacea and Staphylea bumalda were distribute at moist habitats, while Q. monogolica, P. densiflora and Q.variabilis at dry habitats. In continuum analysis, each population occupied different distribution area but it was continuously overlapped. On the successional trends of tree species, it is postulated that Q. mongolica species might dominate the altitudinal zone over 700m.

• Journal of KIISE
• /
• v.41 no.10
• /
• pp.785-791
• /
• 2014

Twitter is one of the most famous SNS(Social Network Service) in the world. Twitter spammer accounts that are created easily by E-mail authentication deliver harmful content to twitter users. This paper presents a spammer detection method that utilizes features based on the relationship between users in twitter. Relationship-based features include friends relationship that represents user preferences and type relationship that represents similarity between users. We compared the performance of the proposed method and conventional spammer detection method on a dataset with 3% to 30% spammer ratio, and the experimental results show that proposed method outperformed conventional method in Naive Bayesian Classification and Decision Tree Learning.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.2
• /
• pp.670-675
• /
• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• KSBB Journal
• /
• v.18 no.1
• /
• pp.39-44
• /
• 2003

A Gene content phylogenetic tree and a 16s rRNA based phylogenetic tree were compared for 33 whole-genome sequenced procaryotes, neighbor joining and bootstrap methods (n=1,000). Ratio of conserved COG (clusters of orthologous groups of proteins) to orthologs revealed that they were within the range of 4.60% (Mezorhizobium loti) or 56.57% (Mycopiasma genitalium). This meant that the ratio was diverse among analyzed procaryotes and indicated the possibility of searching for useful genes. Over 20% of orthologs were independent among the same species. The gene content tree and the 16s rDNA tree showed coincidence and discordance in Archaeabacteria, Proteobacteria and Firmicutes. This might have resulted from non-conservative genes in the gene content phylogenetic tree and horizontal gene transfer. The COG based gene content tree could be regarded as a midway phylogeny based on biochemical tests and nucleotide sequences.

• Journal of Intelligence and Information Systems
• /
• v.14 no.2
• /
• pp.193-206
• /
• 2008

Medical diagnosis can be considered a classification task which classifies disease types from patient's condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent's fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.

• Journal of Intelligence and Information Systems
• /
• v.9 no.1
• /
• pp.139-155
• /
• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Journal of the Korea Computer Industry Society
• /
• v.3 no.8
• /
• pp.1039-1048
• /
• 2002

It is difficult and laborious to distinguish between the original and the plagiarism about the electrical documents or on-line received documents, specially student homeworks because in many case, the homeworks are written on the same subject. Existing methods are not appropriate to solve this problem, which find the most appropriate category using the expression frequency of index term in documents to be classified. In this paper, a new classification method was proposed to distinguish between the original and the plagiarism about documents which were written similarly which is based on the syntagma vector - except the similar syntagma tree structure and non-index term.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.1
• /
• pp.27-35
• /
• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.5
• /
• pp.618-623
• /
• 2011

This paper proposes a novel method to discriminate meaningful tables from decorative one using a composite kernel for handling structural information of tables. In this paper, structural information of a table is extracted with two types of parse trees: context tree and table tree. A context tree contains structural information around a table, while a table tree presents structural information within a table. A composite kernel is proposed to efficiently handle these two types of trees based on a parse tree kernel. The support vector machines with the proposed kernel dised kuish meaningful tables from the decorative ones with rich structural information.