Data prediction and classification is a critical method in medical nutrition data analysis area. As for the characteristics of being intuitive, efficient and easy to understand, the decision tree algorithm is widely u...
详细信息
ISBN:
(纸本)9781479928859
Data prediction and classification is a critical method in medical nutrition data analysis area. As for the characteristics of being intuitive, efficient and easy to understand, the decision tree algorithm is widely used in this field. However, the classification rules extracted from the decision tree are not the most simple and efficient. The paper analyzes the classical decision tree algorithmcart, and proposes a new improved algorithm R2-cart. The core idea of the advanced algorithm is, in order to simplify the classification rules and tree, combining cart algorithm with rough set theory to conduct the attribute and rule reduction on the classification rules of decision tree. The experiment, which compares the Original cart algorithm with the improved algorithm, shows that the improved algorithm has much better classification efficiency with achieving a simple and efficient classification rule set at the same time. This improved algorithm has a potential practical value for large-scale medical nutrition data of classification and predictive analysis.
The purpose of this research is to identify the problems that affect the performance of employees in the company. This research is a type of quantitative research involving data with different types ranging from nomin...
详细信息
ISBN:
(纸本)9781538693780
The purpose of this research is to identify the problems that affect the performance of employees in the company. This research is a type of quantitative research involving data with different types ranging from nominal, continuous, categorical, ordinal and using 15 independent variables and 1 dependent variable. The population in this research are all employees of some companies in Sukabumi Indonesia. The sample used was 120 employees and the determination of the sample used a random sampling method. Methods of data collection in this study were questionnaires, interviews, and documentation. Data analysis method uses cart (Classification and Regression Trees) algorithm because it involves many kinds of data. The results showed that there are five variables that can be used for work motivation, corporate social environment, job type, leadership style, and organizational support (company policy). From these variables, the best formula can be generated to improve the performance of the employee is: increase the motivation of work (X1), making the company more conducive social environment (x11), divide the types of work (X14), which became more specific to be done individually, and if an employee is required to work in a team, it should be supported by specific policy organization/ company (X3). Of the overall variables, the most influential factor in the performance of employees is the motivation of work, when higher the motivation of work, then the possibility of increased employee performance could be higher.
In this paper, a neuro-fuzzy system based on improved cart algorithm (Icart) is presented, in which the Icart algorithm is used to design neuro-fuzzy system. It is worth noting that Icart algorithm partitions the inpu...
详细信息
In this paper, a neuro-fuzzy system based on improved cart algorithm (Icart) is presented, in which the Icart algorithm is used to design neuro-fuzzy system. It is worth noting that Icart algorithm partitions the input space into tree structure adaptively, which avoids the curse of dimensionality (number of rules goes up exponentially with number of input variables). Moreover, it adopts density function to construct the local model for every node in order to overcome the discontinuous boundaries existed in cart algorithm. In addition, a supervised scheme is used to adjust parameters to minimize the network output error and construct more accurate fuzzy model on the basis of the Icart algorithm. Finally, to illustrate the validity of the proposed method, a simulation research and a practical application are done. The results show that the proposed method can provide optimal model structure and parameters for fuzzy modeling, possesses high learning efficiency, and is smoother than cart algorithm. It call be successfully applied to modeling jet fuel endpoint of hydrocracking processing. (C) 2003 Elsevier Science Ltd. All rights reserved.
The learning effect of students is crucial for assessing teaching quality, thus playing a significant role in teaching management. Predicting student achievement is a major challenge in understanding the learning effe...
详细信息
The learning effect of students is crucial for assessing teaching quality, thus playing a significant role in teaching management. Predicting student achievement is a major challenge in understanding the learning effect of students. Currently, many studies have utilized machine learning methods such as the decision tree algorithms C4.5, ID3, cart, J48, random forest, and others. However, few studies have explored the use of the Bagging algorithm in this field. Therefore, this study proposes a classification prediction method for student achievement based on the Bagging-cart algorithm. Initially, the student achievement data is preprocessed, and the Apriori method is applied to mine the strongly associated dataset. The optimal hyper-parameters are determined through grid search to train and predict the Bagging-cart algorithm. Furthermore, the cart, J48, and Bagging-cart algorithms are trained, and their evaluation indicators are compared using a confusion matrix. The results indicate that the Bagging-cart model achieves an accuracy of 98.16%, a recall rate of 91.80%, a precision of 90.83%, and an F1 score of 94.87%. In comparison, the accuracy, precision, and F1 scores are higher than those obtained with cart and J48. Although the recall rate is slightly lower than that of cart by 0.26%, it is 0.52% higher than that of J48. Consequently, this method demonstrates strong predictive capabilities and introduces a new reference method for evaluating students' learning effect.
To take advantages of magnetic sensor technology in terms of cost, size, weight, power consumption and wireless communication, a wireless multi-functional magnetic sensor was designed and developed. Then, a novel meth...
详细信息
To take advantages of magnetic sensor technology in terms of cost, size, weight, power consumption and wireless communication, a wireless multi-functional magnetic sensor was designed and developed. Then, a novel method with single multi-functional magnetic sensor and optimal Minimum Number of Split-sample (MNS)-based Classification and Regression Tree (cart) algorithm was proposed in this paper to classify on-road vehicles. The sensor was deployed on the road to acquire real-time vehicle waveform data. The decision tree model based on cart algorithm was used to execute on-line vehicle classification in the sensor node. Eight speed-independent time-domain waveform features were extracted as the model inputs. This paper trained the decision tree model by using vehicle samples derived from the multi-functional magnetic sensor and pruned the optimal decision tree with a Minimum Error Pruning (MEP) rule to obtain an optimal pruning tree which is more robust to new samples. Some experiments were implemented by different sample sets and classification methods. The results showed that the proposed method achieved on-line vehicle classification in the sensor node. For the field sample sets with two vehicle classes, the average accuracy rates of test samples were 88.9% and 94.4% in the original samples and swapping samples respectively. Besides higher accuracy, the method also has a better sample robustness, which is easy to classify new samples. The comparison results of current methods also showed that the proposed method has some advantages in aspects of accuracy rate, sample robustness and execution time. (C) 2014 Published by Elsevier Ltd.
This paper focuses on the design and development of an expert system for on-line detection of various control chart patterns so as to enable the quality control practitioners to initiate prompt corrective actions for ...
详细信息
This paper focuses on the design and development of an expert system for on-line detection of various control chart patterns so as to enable the quality control practitioners to initiate prompt corrective actions for an out-of-control manufacturing process. Using this expert system developed in Visual BASIC 6, all the nine most commonly observed control chart patterns, e.g., normal, stratification, systematic, increasing trend, decreasing trend, upward shift, downward shift, cyclic, and mixture can be recognized well, employing an optimal set of seven shape features. Based on an observation window of 32 data points, it can plot the control chart, compute the control limits, identify the control chart pattern, calculate the process capability index, determine the maximum run length, and identify the starting point of the maximum run length. After pattern recognition, it can also inform the users about various root assignable causes associated with a particular pattern along with the necessary pre-emptive actions. It opens up wide opportunities for quality improvement and real-time applications in diverse manufacturing processes. This developed expert system is built for a vertical drilling process and its recognition performance is tested using simulated process data.
Two-dimensional materials may be the key to unlock the next technological advancement. There have been many other two-dimensional materials discovered after the discovery of graphene, all with their own unique and int...
详细信息
Two-dimensional materials may be the key to unlock the next technological advancement. There have been many other two-dimensional materials discovered after the discovery of graphene, all with their own unique and interesting properties. One type of two-dimensional materials that were discovered has attracted quite a bit of attention, Phosphorene. Phosphorene is a natural semiconductor with a variable band gap and similar structure to carbon-based graphene. The Field Effect Transistor (FET) structure is employed in this project, where phosphorene has been proposed as the channel material between drain and source. An analytical model is developed assuming that gate voltage variations are proportional to nitrogen dioxide (NO2) gas concentration changes. Furthermore, to obtain other models for the current-voltage(I-V) characteristic, Artificial Neural Network (ANN) and Classification and Regression Trees (cart) algorithms have been employed. Finally, the developed models were compared with the data extracted from experimental work by A. Abbas, which show satisfactory agreement and validity of the proposed models.
A fracture toughness analysis method of nano steel fibre reinforced concrete based on improved locust algorithm was proposed in this paper. Firstly, P-CMOD curve is introduced to analyse the fracture mechanism of nano...
详细信息
A fracture toughness analysis method of nano steel fibre reinforced concrete based on improved locust algorithm was proposed in this paper. Firstly, P-CMOD curve is introduced to analyse the fracture mechanism of nano steel fibre reinforced concrete. Then, the minimum cart tree is trained by cart algorithm, and the objective function of fracture toughness is obtained by iterative training. Finally, the improved locust algorithm is introduced to calculate the fracture energy coefficient of nano steel fibre reinforced concrete, and the softening curve is modified by this algorithm to achieve the fracture toughness analysis of nano steel fibre reinforced concrete. The experimental results show that the concrete fracture toughness of this method is 0.56 Mpa center dot m(-1/2), and the critical crack opening displacement is 0.240 mm. The analytical values are consistent with the actual values. This method improves the analysis effect of fracture toughness and critical crack opening displacement of nano steel fibre reinforced concrete.
In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, di...
详细信息
In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.
This study examines the landfalling tropical cyclones (TCs) over China using state-of-the-art data mining methods (i.e. Finite Mixture Model (FMM) based cluster algorithm and the Classification and Regression Tree (CA...
详细信息
This study examines the landfalling tropical cyclones (TCs) over China using state-of-the-art data mining methods (i.e. Finite Mixture Model (FMM) based cluster algorithm and the Classification and Regression Tree (cart)). Using the 1951-2012 TC best track dataset released by the Shanghai Typhoon Institute of the Chinese Meteorological Administration, the tracks of TCs landfalling over the Chinese coast were classified into three clusters through an FMM. Several climate indices were analysed using the cart algorithm for the three clusters. The prediction model built by cart for summer track frequency was based on a random sampling of the data for 46 years (about 75% of the total years) as the training set with a training accuracy of 100% (Cluster-1), 89.96% (Cluster-2) and 100% (Cluster-3). Data for the remaining 16 years (about 25%) were used for testing with a prediction accuracy of 87.5% (Cluster-1), 62.5% (Cluster-2) and 68.75% (Cluster-3). This study focuses on Cluster-1 of summer TCs landfalling over China for its high frequency, strong intensity, severe impacts and long lifespan. Furthermore, it suggests that the FMM algorithm is effective for track classification of TCs landing over China. In addition, the cart algorithm, which was used to build the prediction model of Cluster-1 for the classification of track frequency, showed high accuracy and its results can be explained and understood easily. It provides a novel framework for forecasting the frequency of TCs landfalling over China.
暂无评论