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.

Multifeature pool importance fusion based GBDT (MPIF-GBDT) for short-term electricity load prediction

Abstract: Feature selection is one of the key factors in predicting. Different feature selection algorithms have their unique preferences for elemental analysis of the data. This results in failing to determine the optimal features when a dataset goes through different feature selection algorithms to get different pools of input features, which in turn affects the prediction quality. To address this problem, the method integrates and fuses the feature importance values of two different feature selection methods. Then the input feature pools are optimized and filtered for the prediction model. Finally, the multifeature pool importance fusion based GBDT (MPIF-GBDT) is developed, which integrates the different feature selection methods and predicts the short-term power load in combination with the gradient boosting decision tree algorithm. In this paper, the tree model feature selection and the Recursive Feature Elimination (RFE) are chosen as feature selection methods. The experimental results show that MPIF-GBDT can significantly improve the accuracy of the prediction compared with the benchmark model.

Efficient Quality Assessment Technique with Integrated Cluster Validation and Decision Trees

Abstract: becomes a key technique in analyzing quality assessment in most of the recent research works. The partitioned clustering techniques used in previous work utilize attributes of objects to form cluster. The cluster numbers were initialized, which reduces cluster quality in terms of cluster object aggregation and appropriation. The work presented an efficient quality assessment technique comprising of two parts i.e., fuzzy k-means cluster validation scheme and decision tree model. The Fuzzy k- means cluster validation scheme improves recall and precision measure of automatically labeling cluster objects. The decision tree model evaluates labeled cluster object and decides on the appropriation of attributes to its cluster validity index. The cluster quality index is measured in terms of number of clusters, number of objects in each cluster, cluster object cohesiveness, precision and recall values. Cluster validates focus on quality metrics of the institution data set features experimented with real and synthetic data sets. The results of quality indexed fuzzy k- means shows better cluster validation compared to that of traditional k-family algorithm. The experimental results of cluster validation scheme and decision tree confirm the reliability of quality validity index which performs better than other traditional k-family clusters.

Analysis of Failure Features of High-Speed Automatic Train Protection System

Abstract: An automatic train protection (ATP) system is the core to ensure operation safety of high-speed railway. At present, failure rate change rules of the system are not well understood and the maintenance strategy is not refined. In order to improve the protection capability and maintenance level of high-speed trains, this paper proposes a decision tree machine learning model for failure feature extraction of ATP systems. First, system type, mean operation mileage, mean service time, etc. are selected as ATP failure feature parameters, and cumulative failure rate is selected as a model output label. Second, support vector machine, AdaBoost, artificial neural networks and decision tree model are adopted to train and test practical failure data. Performance analysis shows that decision tree learning model has better generalization ability. The accuracy of 0.9761 is significantly greater than the other machine learning models. Therefore, it is most suitable for failure features analysis. Third, interpretability analysis reveals the quantitative relationship between system failure and features. Finally, an intelligent maintenance system for ATP systems is built, which realize the refined maintenance throughout life cycle.

Latent Attribute Space Tree Classifiers

Abstract: A framework of latent attribute space tree classifier (LAST) is proposed in this paper. LAST transforms data from the original attribute space into the latent attribute space, which is easier for data separation or more suitable for tree classifier, so that the decision boundary of the traditional decision tree can be extended and its generalization ability can be improved. This paper presents two SVD (singular value decomposition) oblique decision tree (SODT) algorithms based on the LAST framework. SODT first performs SVD on global and/or local data to construct orthogonal latent attribute space. Then, traditional decision tree or tree nodes are built in that space. Finally, SODT obtains the approximately optimal oblique decision tree of the original space. SODT can not only handle datasets with similar or different distribution between global and local data, but also can make full use of the structure information of the labelled and unlabelled data and produce the same classification results no matter how the observations are arranged. Besides, the time complexity of SODT is identical to that of the univariate decision tree. Experimental results show that compared with the traditional univariate decision tree algorithm C4.5 and the oblique decision tree algorithms OC1 and CART-LC, SODT gives higher classification accuracy, more stable decision tree size and comparable tree-construction time as C4.5, which is much less than that of OC1 and CART-LC.

Server fault automatic detection system and detection method based on decision tree

Abstract: The invention discloses a server fault automatic detection system and a detection method based on a decision tree, which combines an expert system and an IPMI management unit to generate a historicaldata set. The server running state data, i.e. Abnormal data stream, is obtained by IPMI management unit. According to the abnormal data stream, the new fault feature vector is extracted, and the new feature vector and the fault cause relation pair are formed into a fault data set, and the fault data set is trained into a self-diagnosing decision tree model. When the server fails while running, thecorresponding fault feature vectors are extracted, the fault types are automatically judged by the self-diagnostic decision tree model, After the fault is cleared, the fault feature vector and the fault cause relation are updated and the self-diagnosing fault tree model is updated. Therefore, the fault diagnosis system will be more accurate and reliable with the improvement of the history fault set.