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.

A fast compressive sensing based N-best class selector for classification applications

Abstract: In this paper, an N-best class selector based on compressive sensing (CS) algorithm and a tree search strategy is introduced and applied for classification applications and its accuracy and complexity are compared with some well-known classifiers. In this approach, classification is done in three steps. At first, the set of most similar training samples for the specific test sample is selected by KD-tree search algorithm. Then, a CS based N-best class selector is used to limit the classifier input into certain classes. This makes the classifier adapt to each test sample and reduces the empirical risk. Finally, a well known low error rate classifier is used to classify the candidate classes. By this approach, we obtain competitive results with promising computational complexity in comparison with state of the art classifiers which causes this approach become a suitable candidate in common classification problems.

Herb Suggestion Model for Treat Disease by Symptoms Using Decision Tree Techniques

Abstract: This research paper presents designing and developing model to support a decision making for choosing the traditional herbal medicine to relieve the symptoms in 8 groups of diseases or symptoms and used 38 attributes for filtering the diseases to generate a model. The attributes information related to the diagnosis of disease or symptoms are collected from the knowledge of the herbal medicine books and online medias for use as a learning and testing dataset of 230 records by using the J48 algorithm to create the rule-based from the decision tree model. After that, the model performance is evaluated with k-fold cross validation method and find a suitable accuracy model in the Weka software. From the experimental results, it is found that the data accuracy is 94.40%, a prediction in conjunction with the precision is 0.950, recall is 0.944, and f-measure is 0.938. It is effective to recommend herbs for the symptomatic treatment in alleviating the initial symptoms.

A New Approach to The Complexity of Decision Problems

Abstract: A new measure of the complexity of optimal economic decisions is introduced. It is based on the level of detail of information (no information; ordinal; and cardinal information) that is required to establish optimality. A detailed example involving sequential group decision making is provided. It is shown that the type of links between successive agents determines the degree of complexity. The measure is also illustrated in the realm of matching problems.

Research on Defect Detection Technology of Trusted Behavior Decision Tree Based on Intelligent Data Semantic Analysis of Massive Data

Abstract: With the rapid development of information technology, software systems' scales and complexity are showing a trend of expansion. The users' needs for the software security, software security reliability and software stability are growing increasingly. At present, the industry has applied machine learning methods to the fields of defect detection to repair and improve software defects through the massive data intelligent semantic analysis or code scanning. The model in machine learning is faced with big difficulty of model building, understanding, and the poor visualization in the field of traditional software defect detection. In view of the above problems, we present a point of view that intelligent semantic analysis technology based on massive data, and using the trusted behavior decision tree model to analyze the soft behavior by layered detection technology. At the same time, it is equipped related test environment to compare the tested software. The result shows that the defect detection technology based on intelligent semantic analysis of massive data is superior to other techniques at the cost of building time and error reported ratio.

Object data processing method and device, computer equipment and storage medium

Abstract: The invention relates to an object data processing method and device, computer equipment and a storage medium. The method relates to a decision tree model in the field of machine learning, and comprises the following steps: converting object behavior data corresponding to each portrait dimension into a strategy effect corresponding to a target strategy according to the proportion of a first hit category belonging to the implementation of the target strategy on a sample object in the object behavior data corresponding to each portrait dimension of the object portrait data; inputting the strategy effect corresponding to each portrait dimension into a decision tree model, and training the decision tree model by taking strategy effect optimization corresponding to portrait dimension combinations represented by nodes in the decision tree model as a target to obtain a group division decision tree; and determining an optimal subdivision group corresponding to the target strategy based on thestrategy effect corresponding to each subdivision group represented from the root node to the leaf node of the group division decision tree. By adopting the method, the efficiency of mining the subdivided group strategy result from the test result can be improved.