Based on the discuss of the basic concept of data mining technology and the decision tree method,combining with the data samples of wind and hailstorm disasters in some counties of Mudanjiang region,the forecasting mo...Based on the discuss of the basic concept of data mining technology and the decision tree method,combining with the data samples of wind and hailstorm disasters in some counties of Mudanjiang region,the forecasting model of agro-meteorological disaster grade was established by adopting the C4.5 classification algorithm of decision tree,which can forecast the direct economic loss degree to provide rational data mining model and obtain effective analysis results.展开更多
AIM: To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS: We used the C4.5 classification algorithm to construct decision trees with d...AIM: To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS: We used the C4.5 classification algorithm to construct decision trees with data from 261 patients with chronic hepatitis C without a liver biopsy. The FibroTest attributes of age, gender, bilirubin, apolipoprotein, haptoglobin, α2 macroglobulin, and γ-glutamyl transpeptidase were used as predictors, and the FibroTest score as the target. For testing, a 10-fold cross validation was used.RESULTS: The overall classification error was 14.9% (accuracy 85.1%). FibroTest's cases with true scores of FO and F4 were classified with very high accuracy (18/20 for FO, 9/9 for FO-1 and 92/96 for F4) and the largest confusion centered on F3. The algorithm produced a set of compound rules out of the ten classification trees and was used to classify the 261 patients. The rules for the classification of patients in FO and F4 were effective in more than 75% of the cases in which they were tested.CONCLUSION: The recognition of clinical subgroups should help to enhance our ability to assess differences in fibrosis scores in clinical studies and improve our understanding of fibrosis progression,展开更多
With the deepening of educational informatization,universities have accumulated massive amounts of student performance data.Extracting valuable information from these data to support instructional decisionmaking has b...With the deepening of educational informatization,universities have accumulated massive amounts of student performance data.Extracting valuable information from these data to support instructional decisionmaking has become an important research topic.This paper constructs a student performance prediction model based on the decision tree algorithm.First,the basic principles of decision tree classification techniques are systematically reviewed,with particular focus on the information gain ratio mechanism and post-pruning method of the C4.5 algorithm.Second,a complete prediction workflow is designed,encompassing data acquisition,preprocessing,feature selection,and model construction and evaluation.Third,an empirical analysis is conducted using student performance data from a university,resulting in a decision tree model with strong interpretability.Finally,the model's performance is evaluated using a confusion matrix.Experimental results indicate that the proposed decision tree model achieves a prediction accuracy exceeding 85%and can effectively identify key factors influencing student performance.The findings provide data support and decision-making references for teachers to improve instructional methods and for universities to optimize teaching management.展开更多
Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the betteri...Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the bettering of ID3 algorithm and constructa data set of the scholarship evaluation system through the analysis of the related attributes in scholarship evaluation information.And also having found some factors that plays a significant role in the growing up of the college students through analysis and re-search of moral education, intellectural education and culture&PE.展开更多
Based on the tropical cyclone(TC) observations in the western North Pacific from 2000 to 2008, this paper adopts the particle swarm optimization(PSO) algorithm of evolutionary computation to optimize one comprehensive...Based on the tropical cyclone(TC) observations in the western North Pacific from 2000 to 2008, this paper adopts the particle swarm optimization(PSO) algorithm of evolutionary computation to optimize one comprehensive classification rule, and apply the optimized classification rule to the forecasting of TC intensity change. In the process of the optimization, the strategy of hierarchical pruning has been adopted in the PSO algorithm to narrow the search area,and thus to enhance the local search ability, i.e. hierarchical PSO algorithm. The TC intensity classification rule involves core attributes including 12-HMWS, MPI, and Rainrate which play vital roles in TC intensity change. The testing accuracy using the new mined rule by hierarchical PSO algorithm reaches 89.6%. The current study shows that the novel classification method for TC intensity change analysis based on hierarchic PSO algorithm is not only easy to explain the source of rule core attributes, but also has great potential to improve the forecasting of TC intensity change.展开更多
Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play...Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.展开更多
分类问题是数据研发领域里研究和使用最广泛的技术之一。近几年经济的飞速发展,分类问题在多行业和领域中被广泛使用,那么,怎样更准确、更有效地分类呢?这是多数研究工作人员的目标。决策树(decision tree)以条理清晰,程序严谨,定量、...分类问题是数据研发领域里研究和使用最广泛的技术之一。近几年经济的飞速发展,分类问题在多行业和领域中被广泛使用,那么,怎样更准确、更有效地分类呢?这是多数研究工作人员的目标。决策树(decision tree)以条理清晰,程序严谨,定量、定性分析相结合,方法通俗易懂,容易掌握,应用性较强等优点,被广泛应用。现在构造决策树的算法比较多,如:ID3算法、C4.5算法、CART等。C4.5算法是在ID3算法的基础上进行改进的,C4.5算法选用信息增益率(Info Gain Ratio)为择取分枝属性的标准,此方法弥补了ID3算法在运用信息增益择取分枝属性时偏向于取值较多的属性的不足之处,但是C4.5算法也有部分缺陷,本文主要在其处理连续属性比较耗时问题上进行深入探讨,本文对其连续的处理过程进行改进来提高C4.5算法的计算效率,大大缩短算法用时。展开更多
基金Supported by Science and Technology Plan of Mudanjiang City (G200920064)Teaching Reform Construction of Mudanjiang Normal University (10-xj11080)
摘要Based on the discuss of the basic concept of data mining technology and the decision tree method,combining with the data samples of wind and hailstorm disasters in some counties of Mudanjiang region,the forecasting model of agro-meteorological disaster grade was established by adopting the C4.5 classification algorithm of decision tree,which can forecast the direct economic loss degree to provide rational data mining model and obtain effective analysis results.
基金Supported by A grant of the Universidad Nacional Autonoma de Mexico SDI.PTID.05.6
摘要AIM: To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS: We used the C4.5 classification algorithm to construct decision trees with data from 261 patients with chronic hepatitis C without a liver biopsy. The FibroTest attributes of age, gender, bilirubin, apolipoprotein, haptoglobin, α2 macroglobulin, and γ-glutamyl transpeptidase were used as predictors, and the FibroTest score as the target. For testing, a 10-fold cross validation was used.RESULTS: The overall classification error was 14.9% (accuracy 85.1%). FibroTest's cases with true scores of FO and F4 were classified with very high accuracy (18/20 for FO, 9/9 for FO-1 and 92/96 for F4) and the largest confusion centered on F3. The algorithm produced a set of compound rules out of the ten classification trees and was used to classify the 261 patients. The rules for the classification of patients in FO and F4 were effective in more than 75% of the cases in which they were tested.CONCLUSION: The recognition of clinical subgroups should help to enhance our ability to assess differences in fibrosis scores in clinical studies and improve our understanding of fibrosis progression,
摘要With the deepening of educational informatization,universities have accumulated massive amounts of student performance data.Extracting valuable information from these data to support instructional decisionmaking has become an important research topic.This paper constructs a student performance prediction model based on the decision tree algorithm.First,the basic principles of decision tree classification techniques are systematically reviewed,with particular focus on the information gain ratio mechanism and post-pruning method of the C4.5 algorithm.Second,a complete prediction workflow is designed,encompassing data acquisition,preprocessing,feature selection,and model construction and evaluation.Third,an empirical analysis is conducted using student performance data from a university,resulting in a decision tree model with strong interpretability.Finally,the model's performance is evaluated using a confusion matrix.Experimental results indicate that the proposed decision tree model achieves a prediction accuracy exceeding 85%and can effectively identify key factors influencing student performance.The findings provide data support and decision-making references for teachers to improve instructional methods and for universities to optimize teaching management.
摘要Under the modern education system of China, the annual scholarship evaluation is a vital thing for many of the collegestudents. This paper adopts the classification algorithm of decision tree C4.5 based on the bettering of ID3 algorithm and constructa data set of the scholarship evaluation system through the analysis of the related attributes in scholarship evaluation information.And also having found some factors that plays a significant role in the growing up of the college students through analysis and re-search of moral education, intellectural education and culture&PE.
基金National Natural Science Foundation of China(41201045)Jiangsu Qing Lan Project(2016)Natural Science Foundation of Jiangsu Province(BK20151458)
摘要Based on the tropical cyclone(TC) observations in the western North Pacific from 2000 to 2008, this paper adopts the particle swarm optimization(PSO) algorithm of evolutionary computation to optimize one comprehensive classification rule, and apply the optimized classification rule to the forecasting of TC intensity change. In the process of the optimization, the strategy of hierarchical pruning has been adopted in the PSO algorithm to narrow the search area,and thus to enhance the local search ability, i.e. hierarchical PSO algorithm. The TC intensity classification rule involves core attributes including 12-HMWS, MPI, and Rainrate which play vital roles in TC intensity change. The testing accuracy using the new mined rule by hierarchical PSO algorithm reaches 89.6%. The current study shows that the novel classification method for TC intensity change analysis based on hierarchic PSO algorithm is not only easy to explain the source of rule core attributes, but also has great potential to improve the forecasting of TC intensity change.
基金sponsored by the National Science and Technology Major Project(No.2011ZX05023-005-006)
摘要Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.
摘要分类问题是数据研发领域里研究和使用最广泛的技术之一。近几年经济的飞速发展,分类问题在多行业和领域中被广泛使用,那么,怎样更准确、更有效地分类呢?这是多数研究工作人员的目标。决策树(decision tree)以条理清晰,程序严谨,定量、定性分析相结合,方法通俗易懂,容易掌握,应用性较强等优点,被广泛应用。现在构造决策树的算法比较多,如:ID3算法、C4.5算法、CART等。C4.5算法是在ID3算法的基础上进行改进的,C4.5算法选用信息增益率(Info Gain Ratio)为择取分枝属性的标准,此方法弥补了ID3算法在运用信息增益择取分枝属性时偏向于取值较多的属性的不足之处,但是C4.5算法也有部分缺陷,本文主要在其处理连续属性比较耗时问题上进行深入探讨,本文对其连续的处理过程进行改进来提高C4.5算法的计算效率,大大缩短算法用时。