Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

From Min Tree to Watershed Lake Tree: Evaluation

Abstract: Recently, several tree based hierarchical image descriptions have been proposed for image segmentation and analysis. This paper considers the problem of evaluating such algorithms. Recently we proposed a new algorithm for constructing the watershed lake tree by transforming the min tree structure as these two image trees share some similarities. We use this algorithm to illustrate the evaluation approach. The algorithm is evaluated by considering its computational complexity, memory usage and the cost of manipulating the resulting tree structure. Our results show that considerable care is needed when evaluating algorithms of this kind. In particular, comparisons cannot be made simply on the basis of computational complexity alone and other parameters such as image/tree ‘complexity’ also need to be considered.

Lake water quality prediction method based on decision tree algorithm

Abstract: The invention discloses a lake water quality prediction method based on a decision tree algorithm, and relates to the technical field of water quality detection The prediction method comprises the steps of obtaining sample data, dividing the sample data into a training set and a verification set, and determining a category to which corresponding water quality data belongs; establishing a decisiontree model by utilizing the sample data of the training set and the output water quality category, and taking the water level of the station as a leaf node discrimination standard; on the basis of the established decision tree model and the leaf node discrimination standard, inputting sample data of the verification set, and outputting a predicted water quality category; and comparing the predicted water quality category with the actually measured water quality category to verify the water quality prediction effect of the decision tree model According to the method, the correlation between the station water level and the lake water quality category is innovatively constructed, compared with a traditional water quality forecasting method, the lake water quality category under the future condition is better forecasted, the result is reliable, and the method has more practical significance

Classification of Indonesian Government Budget Appropriations or Outlays for Research and Development (GBAORD) Using Decision Tree and Naive Bayes

Abstract: Indonesian Government Budget Appropriations or Outlays for Research and Development (GBAORD) is one of component from Indonesian Gross Expenditure on Research and Development (GERD). Calculation of GBAORD are made by classifying each government expenditure budget. Classification is done manually so that there are frequent inconsistencies. This study proposed an automated classification using decision tree and Naive bayes algorithm to handle problem in previous method. The result of study have conclusion that an automated classification using Naive bayes is more accurate than using decision tree. The highest average accuracy score given by Naive Bayes model in this experiment is 98.462 while decision tree gives about 90.236. This can be related to the number of features involved in classification process. Naive Bayes uses most of the features in data while decision tree only uses one feature. Although decision tree model only uses one feature, the accuracy score produced by the model is considered as high.

An Intention Tree based Model for Social Needs

Abstract: With the application of more and more new technologies, such as cloud computing, IoT, and big data, the available intelligent service scenarios become complex. For better service identification and recommendation, conversational AI bots need to construct user needs expression models accurately. Most traditional user needs expression models could do better in individual tasks but nearly not support the social requirements. To solve the challenge before, this study expands the Intention Tree Model for Personal Needs(ITM-PN) to the Intention Tree Model for Social Needs based on Negotiation(ITM-SN) to model different users’ needs during the process of multi-user negotiation(initial, processing, ending). And finally, this study conducted a case study to prove the ITM-SN is thoroughly and effectively, which could be widely applicable in many fields.

Building and Querying a Decision Tree Model with Constraint Logic Programming

Abstract: Decision tree induction has gained its popularity as an effective automated method for data classification mainly because of its simple, easy-to-understand, and noise-tolerant characteristics. The induced tree reveals the most informative attributes that can best characterize training data and accurately predict classes of unseen data. Despite its predictive power, the tree structure can be overly expanded or deeply grown when the training data do not show explicit patterns. Such bushy and deep trees are difficult to comprehend and interpret by humans. We thus propose a logic-based method to query over a complicate tree structure to extract only parts of the tree model that are really relevant to users’ interest. The implementation using ECLiPSe constraint language to perform constrained search over a decision tree model is given in this paper. The illustrative examples on medical domains support our hypothesis regarding simplicity of constrained tree-based patterns.