Enhanced Decision Tree Algorithm Research Articles

Optimization Method of University English Guidance Based on Enhanced Decision Tree Model in the Context of Big Data

The development of economy has propelled the university’s English guidance system to pay attention to students’ development which further impacts the development of the country because students are the future of any nation. The college English function is rather complicated, and thus, reunderstanding and positioning of the university’s English guidance plays a significant role in the field of education. The existing teaching guidance system puts more emphasis on teaching content and teaching mechanism resulting in lack of application of the knowledge gained. Thus, it is extremely important to clarify and highlight the objectives of college English learning and on the basis of these design personalized curriculum which could convert the skilled talents into compound ones. The present paper explores the evaluation model of the University English Guidance effect based on an enhanced decision tree algorithm. The model has the potential to improve accuracy and efficiency of University English Guidance effect evaluation system and meet the requirements of University English Guidance effect evaluation. The framework constructs a multi-index University English Guidance effect evaluation system constituting of teachers and students as the main entities. It considers the University English Guidance effect evaluation index data as the input sample and implements the least squares support vector machine to realize the University English Guidance effect evaluation. The effectiveness of this model is verified by experiments, which lays a foundation for the optimization of University English Guidance.

An efficient automatic accident detection system using inertial measurement through machine learning techniques for powered two wheelers

The two-wheeler accidents in most populated and developing countries have become vulnerable and six accidents happen every hour on average. This paper proposes an efficient automatic accident detection system that attempts to detect the occurrences of the accidents in powered two-wheelers (PTW) automatically using vehicle-dependent parameters and the physiological parameters of the rider in real-time. The proposed system builds an accident detection system in PTW using three steps namely, critical event detection system, accident detection system, and severity assessment system. The critical event detection system reads the accelerometer sensor values from the On-Board Diagnostic (OBD) unit mounted on the PTW and classifies the state of the vehicle as normal, fall-like, and fall through the enhanced decision tree algorithm. The enhanced decision tree algorithm uses a tanh function to calculate entropy values. The rules are extracted to fix the threshold by pruning the decision tree to identify the fall of the vehicle and the rider. Due to the unstable nature of PTW and the rider, a novel Adaptive Sequence Window algorithm (ASW) is proposed to substantiate and validate the occurrence of accidents based on the sequence of states identified. Once the accident is detected, the Decision Support System (DSS) running on the OBD mounted on the PTW decides the severity of the accident by combining the three parameters namely fall of the vehicle, fall of the rider, and pulse rate of the rider using the first-order predicate logic rules. The enhanced decision tree algorithm outperforms the other classifiers such as naïve Bayes, artificial neural network, and recurrent neural network with an accuracy of 99.8%. The OBD unit mounted on the PTW and the rider’s helmet is used to detect the occurrence of accidents automatically along with its severity with less time. The ASW algorithm enables the system to detect the fall of the vehicle and rider within five minutes and prevents false positives. Further, the information can be communicated based on the severity of the accident to the emergency medical service for quick response.

Classification of Existing Health Model of India at the End of the Twelfth Plan using Enhanced Decision Tree Algorithm

The high rate of urbanisation has increased the need for state-of-art health models that can meet the growing needs of society during any pandemic. Information-theoretic algorithms based on decision tree can mine the data to establish standards for the final decision by classifying the related data. Classification is an effective tool to analyse the existing health system in India’s states and union territories. For this purpose, the data is categorised and then treated with the enhanced Shannon Entropy-based C4.5 decision tree algorithm to set some rules. These rules are capable of finding the major gaps in the health care systems after the analysis. Supposedly, these gaps are taken care of properly in the affected regions. In that case, the health care models will accomplish the endeavouring Sustainable Development Goals.

Enhanced Decision Tree Algorithm for Discovering Intra and Inter Class Exceptions

Decision tree algorithms, being accurate and comprehensible classifiers, have been one of the most widely used classifiers in data mining and machine learning. However, like many other classification algorithms, decision tree algorithms focus on extracting patterns with high generality and in the process, these ignore some rare but useful and interesting patterns that may exist in small disjuncts of data. Such extraordinary patterns with low support and high confidence capture very specific but exceptional behavior present in data. This paper proposes a novel Enhanced Decision Tree Algorithm for Discovering Intra and Inter-class Exceptions (EDTADE). Intra-class exceptions cover objects of unique interest within a class whereas inter-class exceptions capture rare conditions due to which we are forced shift the class of few unusual objects. For instance, whales and bats are examples of intra-class exceptions since these have unique characteristics within the class of mammals. Further, most of the birds are flying creatures, but the rare birds, like penguin and ostrich fall in the category of no flying birds. Here, penguin and ostrich are inter-class exceptions. In fact, without knowing about such exceptional patterns, our knowledge about a domain is incomplete. We have enhanced the decision tree algorithm by defining a framework for capturing intra and inter-class exceptions at leaf nodes of a decision tree. The proposed algorithm (EDTADE) is applied to many datasets from UCI Machine Learning Repository. The results show that the EDTADE has been successful in discovering many intra and inter-class exceptions. The decision tree augmented with intra and inter-class exceptions are more accurate, comprehensible as well as interesting since these provide additional knowledge in the form of exceptional patterns that deviate from the general rules discovered for classification.

Urban expansion modeling using an enhanced decision tree algorithm

Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area.

Enhanced decision tree algorithm using genetic algorithm for heart disease prediction

In today's present scenario, heart disease has greater impact on our lives and identified fatal due to its high mortality rate. The diagnosis of heart disease is more challenging due to its vulnerability. Gone to limitation of previous work of literature survey, enhanced decision tree algorithm is introduced and applied on University of California, Irvine data sets. In order to predict heart disease, enhanced decision tree algorithm generates the decision rules which are later optimised by genetic algorithm. By then we examine the methods and operators of the algorithm. Finally our proposed algorithm is compared with decision tree (C4.5) and support vector machine algorithm, the proposed algorithm shows high accuracy, and its simplicity makes ideal for pattern recognition applications.

Enhanced decision tree algorithm using genetic algorithm for heart disease prediction

In today's present scenario, heart disease has greater impact on our lives and identified fatal due to its high mortality rate. The diagnosis of heart disease is more challenging due to its vulnerability. Gone to limitation of previous work of literature survey, enhanced decision tree algorithm is introduced and applied on University of California, Irvine data sets. In order to predict heart disease, enhanced decision tree algorithm generates the decision rules which are later optimised by genetic algorithm. By then we examine the methods and operators of the algorithm. Finally our proposed algorithm is compared with decision tree (C4.5) and support vector machine algorithm, the proposed algorithm shows high accuracy, and its simplicity makes ideal for pattern recognition applications.

Analysis of Factors of Road Traffic Accidents using Enhanced Decision Tree Algorithm

tree is a data mining technique which is used to build classification model. ID3 and C4.5 are basic decision tree algorithms that are used to create classification model depending upon the different datasets. In this paper, the enhanced C4.5 algorithm is presented to analyze the traffic accident data using WEKA data mining tool. The main aim of this enhanced approach is to provide a simple, efficient model in classifying the data set. The efficiency of enhanced algorithm is drawn by comparing it with existing algorithms. KeywordsID3, Decision tree algorithm, NH (National highway), WEKA tool

Enhanced Decision Tree Algorithm for Data Streams using adaptation of Concept Drift

Enhanced Decision Tree Algorithm for Data Streams using adaptation of Concept Drift

Enhanced Decision Tree Algorithm for Data Streams using adaptation of Concept Drift

Construction of a decision tree is a well researched problem in data mining. Mining of streaming data is a very useful and necessary application. Algorithms such as VFDT and CVFDT are used for decision tree construction, but as a lot of new examples are added, a new optimal model needs to be constructed. Here in this paper, we have provided an algorithm for decision tree construction which uses discriminant analysis, to select the cut point used for splitting tests, thus optimizing time complexity from O(nlogn) to O(n). We have also analyzed several learning strategies such as dynamic ensemble, contextual, forgetting and detection approaches. We have also discussed handling of concept drift which occurs due to gradual change in the data set using the naive Bayes classifer at each of the inner node.

Reengineering claims processing using probabilistic inductive learning

With health care costs in the United States skyrocketing, and $.25 of every health care dollar being spent on systems and claims administration, technological advances such as electronic claims filing are being advocated as cost-reducing measures. These improvements alone, however, will not significantly reduce costs unless they are accompanied by revisions in the entire claims processing system. This study explores the reliability and utility of probabilistic inductive learning (PrIL), a statistically enhanced decision tree algorithm, for improving the decision-making process at the New York State Workers' Compensation Board (WCB). Results indicate that the PrIL algorithm is favorably comparable to both the purely statistical and the classical decision tree methodologies, with the added advantages of easy to understand rules and user-defined reliability measures for each of those rules. Given the appropriate information regarding the relative value of correct and incorrect classification of cases in the WCB system, PrIL can be used to accurately assist in the decision making process in terms of reducing cost, predicting and enhancing quality and case outcomes in managed care practices.