- 著者
- Thitiprayoonwongse Daranee Suriyaphol Prapat Nuanwan Soonthornphisaj
- 出版者
- 人工知能学会
- 雑誌
- 人工知能学会全国大会論文集 (ISSN:13479881)
- 巻号頁・発行日
- vol.26, 2012
Dengue infection is an epidemic disease typically found in tropical region. Symptoms of this infection show rapid and violent to patients in a short time. There are 4 classes of Dengue infections which are DF, DHF I, DHF II, and DHF III. Nowadays, the experts need to know the set of features on dengue infection in order to correctly classify the patients. Our temporal dataset consists of clinical data and laboratory data. The data was collected from the first visit of patient until the date of discharge. We obtained 3 datasets from different regions of Thailand which are Srinagarindra Hospital (KK: 440 patients), Songklanagarind Hospital ( SK : 330 patients) and Siriraj Hospital (SR: 258 patients). Each dataset consists of more than 400 attributes. The second objective of this research is to detect the day of defervescence of fever which is called day0. The day0 date is the critical date of Dengue patients that some patients face the fatal condition. Therefore the physicians need to know the feature sets, those have effect on the condition. They expect to have an intelligent system that can trigger the day0 date of each patient. To accomplish the knowledge discovery task, we consider to employ decision tree as a data mining tool. We propose a set of meaningful attributes from the temporal data. We analyzed the result of dengue's decision tree and day0's decision tree in discussion part. Finally, we obtained high accuracy (97.0 %) and we got the new set of features that can be applied to real world data.
- 著者
- Prachuabsupakij Wanthanee Nuanwan Soonthornphisaj
- 出版者
- 人工知能学会
- 雑誌
- 人工知能学会全国大会論文集 (ISSN:13479881)
- 巻号頁・発行日
- vol.26, 2012
The aim of this paper is to improve the classification performance based on the multiclass imbalanced datasets. In this paper, we introduce a new classification technique based on Clustering approach for Imbalanced Multiclass datasets (CIM). CIM uses the clustering approach to create a new training set for each cluster and apply two re-sampling technique to re-balance the class distribution. CIM improves the classification performance based on the multiclass imbalanced datasets in three ways. Firstly, k-means is used to split the set of instances into two clusters. Then, for each cluster, two re-sampling tehcnique (oversampling and undersampling) are applied on the the training set in order to balance the class distribution. Finally, ensemble approaches are used to combine the models obtained with our method through a majority vote. We have conducted experiments on many multiclass datasets from the UCI. These datasets consist of two types of class distribution; balance and imbalance. We use different classifiers in order to observe the performance and suitability of our purpose within each classifier. We carry out the experimental study with the several well-know algorithms such as Decision Trees, Naïve Bayes, and K-Nearest Neighbors . The performance is measured based on G-mean and F-measure. The experimental results show that the proposed method achieved higher performance than the baseline algorithms; One-Against-One, One-Against-All, and Error-Correcting-Output-Coding (ECOC), and the baseline with oversampling algorithms in many classifiers. Moreover, the empirical results show that CIM algorithm is a practical algorithm since it can be applied to both balance and imbalance datasets. The proposed method was successfully applied to many datasets. Since CIM creates the new training sets that consist of the instances with similar characteristics and these instances are relabeled.