A Decision Tree Classification Algorithm Based on Two-Term RS-Entropy.

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.

The student group is the hierarchical talent of socialist modernization with Chinese characteristics in the new era, and is an important talent guarantee for the prosperity and development of the country. By exploring the implementation path of the evaluation index system for civic politics, a proposal has been made for an evaluation index system for college students’ civic politics education. The traditional ID3 decision tree algorithm is optimized for the problem of the long time that may occur when discretizing the evaluation data, and the improved algorithm is used to generate the decision tree of college students’ civic and political education work, and eight effective indicators are screened. The G1 and CRITIC methods were selected to calculate subjective and objective weights, and then the G1-CRITIC model was established to determine the index combination weights using the game theory combination assignment method. The results of the study show that the highest percentage of the seven evaluation indicators is the one-time employment rate (0.21099), which indicates that the research content of this paper provides a reference for universities to carry out the work of Civic and Political Education.

The organization of a web site is important to help users get the most out of the site. Designing such an organization, however, is a complicated problem. Traditionally, this design is mainly done by hand. To what extent this can be automated is a challenging problem. Recently, there have been investigations on how to reorganize an existing web site based on some criteria. But none of them has addressed the problem of organizing a web site automatically from scratch. In this paper, we attempt to tackle this problem by restricting the domain to online catalog organization.We model an online catalog organization as a decision tree structure and propose a metric, based on the popularity of products and the relative importance of product attribute values, to evaluate the quality of a catalog organization. The problem is then formulated as a decision tree construction problem. Although traditional decision tree algorithms, such as C4.5, can be used to generate online catalog organization, the catalog constructed is generally not good based on our metric. An efficient greedy algorithm (GENCAT) is thus developed and the experimental results show that GENCAT produces better catalog organizations based on our metric.KeywordsMobile PhoneGreedy AlgorithmAutomatic ConstructionOnline CatalogPopular ProductThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Energy spectra unfolding of fast neutron sources using the group method of data handling and decision tree algorithms

Structured continuous-label classification is a variety of classification in which the label is continuous in the data, but the goal is to classify data into classes that are a set of predefined ranges and can be organised in a hierarchy. In traditional decision (classification) tree algorithms, categorical (class) variable as the label is assumed. When the label is a continuous variable in the data, two achievable approaches based on existing DT algorithms can be used to handle the situations. For the first approach uses a data discretisation method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of non-overlapping intervals and then applies a DT algorithm. In the second approach using the continuous label directly, simply applies a regression tree algorithm. These approaches have their own drawbacks. In the hierarchy, the ranges at the lower levels are more specific and inherently more difficult to predict, whereas the ranges at the upper levels are less specific and inherently easier to predict. Therefore, both prediction specificity and prediction accuracy must be considered when building a DT from this kind of data. In this survey paper, we survey the classification techniques related to the continuous label. More classification approaches are there in the literature. The main goal of the survey is present an overview of previous classification techniques. Various methods that are written by the different authors are presented in this survey.

Aim: Chronic kidney disease (CKD) is among the main 20 reasons for death worldwide and influences around 10% of the world grown-up populace. CKD is an issue that upsets typical kidney work. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Linear Regression to find out which of these can give us the best accuracy. Material and Methods: The study used 322 samples with Novel Decision Tree and Linear Regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Linear Regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Linear Regression(LR) with improved accuracy.

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

In view of the fact that the existing decision tree models have not considered the decision makers’ preference behaviors during the classification process, these models cannot predict the problems with obvious preference very well. Driven by demand, this paper puts forward a new decision tree algorithm—a preference cost sensitive decision tree (PCSDT). This algorithm introduces the preference degree and preference cost to decision tree. Then, we utilize the effective preference to construct a new attribute selection factor (ASF) and the class label distribution rule for a node. Finally, a preference sensitive decision tree with best preference degree is generated by adaptively adjusting the preference degree. Experiments have proved that the proposed method can guarantee better overall accuracy and performances than other traditional algorithms.

Lazy decision tree (LazyDT) constructs a customized decision tree for each test instance, which consists of only a single path from the root to a leaf node. LazyDT has two strengths in comparison with eager decision trees. One is that LazyDT can build shorter decision paths than eager decision trees, and the other is that LazyDT can avoid unnecessary data fragmentation. However, the split criterion used for constructing a customized tree in LazyDT is information gain, which is skew-sensitive. When learning from imbalanced data sets, class imbalance impedes their ability to learn the minority class concept. In this paper, we use Hellinger distance and K-L divergence as split criteria to build two types of lazy decision trees. An experimental framework is performed across a wide range of imbalanced data sets to investigate the effectiveness of our methods when comparing with the other methods including lazy decision tree, C4.5, Hellinger distance based decision tree and support vector machine. In addition, we also use SMOTE to preprocess the highly imbalance data sets in the experiment and evaluate its effectiveness. The experimental results, which contrasted through nonparametric statistical tests, demonstrate that using Hellinger distance and K-L divergence as the split criterion can improve the performances of LazyDT for imbalanced classification effectively.

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry.

While analyzing the data, it is crucial to choose the model that best matches the circumstance. Many experts in the field of classification and regression have proposed ensemble strategies for tabular data, as well as various approaches to classification and regression problems. In this paper, Gini Index is applied on raw geographical dataset to convert continuous data into discrete dataset. Decision tree algorithm is implemented on resultant discrete dataset, Information Gain is calculated for every attribute and the attribute with highest information gain is the splitting node, applied recursively. Decision tree algorithm implemented predicts the rainfall in Kashmir province with the accuracy of 81.5%. MDL pruning is applied on the resultant decision tree in order to reduce the size & complexity of the Decision tree. Pruning removes segments of the tree that contribute little towards classification; the accuracy is marginally reduced to 81.1%. Furthermore, after the implementation of Decision tree a boosting algorithm: gradient boosting has been implemented on the same set of data using decision tree as a base estimator. It was observed that the overall accuracy of the decision tree got increased to 87.5% after the implementation of gradient boosting model. Thus, the obtained results predict that gradient boosted-DT outperforms all other approaches with the highest accuracy measure and high susceptibility rate in rainfall prediction.

Mixture design optimization and machine learning-based prediction of Al-Mg alloy composite reinforced by Zn nanoparticles: A molecular dynamics study

Ultra-wideband radar signals have broad application prospects in ultra-close-range detection due to its high resolution, strong penetrability and strong anti-interference. Ultra-wideband radar is an ideal detection method in non-contact medical monitoring scenarios. Based on UWB radar, the time domain sliding window feature extraction method and decision tree classification algorithm are used to classify and recognize the human motion state behind the obstacle. The decision tree is generated by inputting the sliding window feature extraction result of the radar echo data into the decision tree algorithm. The experimental results show that the proposed feature extraction algorithm and decision tree algorithm can classify the motion state of the human target behind the obstacle, such as the human walking in the radial direction, walking in the tangential direction, and standing in the ground.

Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using Decision Tree Algorithm and Naive Bayes Algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using Decision tree algorithm and Naive Bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.

The virtualization of computing resources provided by containers has gained increasing attention and has been widely used in cloud computing. This new demand for container technology has been growing and the use of Docker and Kubernetes is considerable. According to recent technology surveys, containers are now mainstream. However, currently, one of the major challenges rises from the fact that multiple containers, with different owners, may cohabit on the same host. In container-based multi-tenant environments, security issues are of major concern. In this paper we investigate the performance of container-level anomaly-based intrusion detection systems for multi-tenant applications. We investigate the use of Bag of System Calls (BoSC) technique and the sliding window with the classifier and we consider eight machine learning algorithms for classification purposes. We show that among the eight machine learning algorithms, the best classification results are obtained with Decision Tree and Random Forest which lead to an F-Measure of 99.8%, using a sliding window with a size of 30 and the BoSC algorithm in both cases. We also show that, although both Decision Tree and Random Forest algorithms leads to the best classification results, the Decision Tree algorithm has a shorter execution time and consumes less CPU and memory than the Random Forest.