Enhanced Decision Tree Research Articles

Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives

AbstractCarbon emissions limit the output of traditional fuel‐fired generating units, significantly affecting the new power system scheduling mechanism. This paper proposes a short‐term electric power and energy balance optimization scheduling method with low‐carbon bilateral demand response (LCBDR). The LCBDR mechanism framework is constructed by combining the analysis of short‐term electric power and energy balance of the system under a dual perspective, along with the electric‐carbon coupling mechanism of the dynamic scheduling on the source‐load side. Based on the carbon emission flow (CEF) theory, the carbon emission index information of load‐side users is obtained. An optimal scheduling model of LCBDR is established. The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. Comprehensive case studies from three different perspectives verify that this method can effectively realize the low‐carbon economic operation of the system, with the peak net load reduced by 24.02% and valley net load increased by 20.43%. Compared with a single perspective, the total operational costs can be reduced by 5.27%, and the carbon emissions of users can be reduced by 5.70%.

TTTS: Tree Test Time Simulation for Enhancing Decision Tree Robustness against Adversarial Examples

Decision trees are widely used for addressing learning tasks involving tabular data. Yet, they are susceptible to adversarial attacks. In this paper, we present Tree Test Time Simulation (TTTS), a novel inference-time methodology that incorporates Monte Carlo simulations into decision trees to enhance their robustness. TTTS introduces a probabilistic modification to the decision path, without altering the underlying tree structure. Our comprehensive empirical analysis of 50 datasets yields promising results. Without the presence of any attacks, TTTS has successfully improved model performance from an AUC of 0.714 to 0.773. Under the challenging conditions of white-box attacks, TTTS demonstrated its robustness by boosting performance from an AUC of 0.337 to 0.680. Even when subjected to black-box attacks, TTTS maintains high accuracy and enhances the model's performance from an AUC of 0.628 to 0.719. Compared to defenses such as Feature Squeezing, TTTS proves to be much more effective. We also found that TTTS exhibits similar robustness in decision forest settings across different attacks.

A powerful Peripheral Arterial Disease detection using machine learning-based severity level classification model and hyper parameter optimization methods

This study addresses the critical need for improved detection and assessment of Peripheral Arterial Disease (PAD) using Artificial Intelligence (AI) algorithms, aiming to reduce global mortality rates associated with this condition. The research focuses on enhancing the efficiency of machine learning (ML) models for PAD detection and severity categorization by employing optimization techniques. Notably, this study introduces the application of Synthetic Minority Oversampling Technique (SMOTE) to mitigate imbalanced data challenges in PAD detection. The primary objective of this research is to develop highly efficient ML models utilizing six distinct AI classifiers and Hyper Parameter Optimization (HPO), specifically targeting PAD identification and severity level categorization. Leveraging the Cleveland and Statlog datasets, the study constructs and evaluates these models using all relevant features. The principal findings indicate remarkable advancements in PAD detection accuracy. The proposed models, optimized using SMOTE and Enhanced Decision Tree (EDT) through HPO, surpass state-of-the-art methodologies, achieving exceptional accuracies of 99.20% and 98.52% on the Cleveland and Statlog datasets, respectively. These models outperform existing approaches, assessed using comprehensive metrics such as F1 score, Accuracy, Precision, Recall, Specificity, and MCC. In conclusion, this study's innovative approach significantly improves the capability to assess PAD and categorize its severity levels accurately. The proposed models hold promise in aiding physicians to evaluate patients' cardiac conditions effectively, enabling early interventions that have the potential to mitigate mortality rates associated with cardiovascular diseases.

Variable-based Fault Localization via Enhanced Decision Tree

Fault localization, aiming at localizing the root cause of the bug under repair, has been a longstanding research topic. Although many approaches have been proposed in past decades, most of the existing studies work at coarse-grained statement or method levels with very limited insights about how to repair the bug ( granularity problem ), but few studies target the finer-grained fault localization. In this article, we target the granularity problem and propose a novel finer-grained variable-level fault localization technique. Specifically, the basic idea of our approach is that fault-relevant variables may exhibit different values in failed and passed test runs, and variables that have higher discrimination ability have a larger possibility to be the root causes of the failure. Based on this, we propose a program-dependency-enhanced decision tree model to boost the identification of fault-relevant variables via discriminating failed and passed test cases based on the variable values. To evaluate the effectiveness of our approach, we have implemented it in a tool called VarDT and conducted an extensive study over the Defects4J benchmark. The results show that VarDT outperforms the state-of-the-art fault localization approaches with at least 268.4% improvement in terms of bugs located at Top-1, and the average improvement is 351.3%. Besides, to investigate whether our finer-grained fault localization result can further improve the effectiveness of downstream APR techniques, we have adapted VarDT to the application of patch filtering, where we use the variables located by VarDT to filter incorrect patches. The results denote that VarDT outperforms the state-of-the-art PATCH-SIM and BATS by filtering 14.8% and 181.8% more incorrect patches, respectively, demonstrating the effectiveness of our approach. It also provides a new way of thinking for improving automatic program repair techniques.

ARTIFICIAL INTELLIGENCE-ENABLED KNOWLEDGE MANAGEMENT USING A MULTIDIMENSIONAL ANALYTICAL FRAMEWORK OF VISUALIZATIONS

To better manage human resources (HR), companies are increasingly incorporating artificial intelligence (AI) and other AI-based tools into their HR management (HRM) strategies, at a universal scale. Companies on a global scale, highlight the employment prospects and use of resources, business judgment, and make predictions using machine learning approaches. This work aims at the situation that the human resource department faces high employee turnover in the company especially some experienced employees leave. The termination of an employee is predicted by using an enhanced ID3 decision tree with ABC rule miner. The best-classifying attributes are chosen by ID3 and association rules are mined to generate an enhanced decision tree to perform classification. It is then passed to the regressor model to make prediction. Gradient descent optimizer is used for optimizing the proposed machine learning model. Predictive analysis is done in HR dataset v-14 by visualizing and analyzing and exploiting the behavioral relationship among the attributes. The variables of employee termination are predicted by a data-driven predictive analysis from the performance measure metrics.

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.

Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification

The diagnosis of Parkinson's disease (PD) is important in neurological pathology for appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been widely used for diagnosing PD through biomedical voice disorders. However, DTI for PD diagnosis is based on a greedy search algorithm which causes overfitting and inferior solutions. This paper improved the performance of DTI using evolutionary-based genetic algorithms. The goal was to combine evolutionary techniques, namely, a genetic algorithm (GA) and genetic programming (GP), with a decision tree algorithm (J48) to improve the classification performance. The developed model was applied to a real biomedical dataset for the diagnosis of PD. The results showed that the accuracy of the J48, was improved from 80.51% to 89.23% and to 90.76% using the GA and GP, respectively.

MOOC and Flipped Classroom Task-Based English Teaching Model for Colleges and Universities Using Data Mining and Few-Shot Learning Technology.

As a revolutionary education model, the flipped classroom teaching model has unique advantages over the traditional education model. How to change the teaching method of flipped classroom into a teaching method suitable for college English courses is a problem. The goal of this research is to investigate how to use data mining and few-shot learning technology to investigate the impact of MOOC and flipped classroom task-based college English teaching modes. This work provides a data mining-based decision tree algorithm and examines the enhanced decision tree method. The experimental results of this study demonstrate that two students in each of the two groups believe that the English teaching mode, which is mostly taught by traditional teachers, is very favorable to a thorough understanding of basic knowledge, accounting for 4% of the total. There are 17 students that believe the new teaching methodology is really beneficial, accounting for 34% of the total. This mode was determined to be effective for in-depth knowledge of the basics by a total of 25 students. It can be seen that the flipped classroom model is more popular with students.

Predictive Model for Type II Diabetes: Deep Learning with Probability Density Function

Abstract: Diabetes mellitus is a scientific ailment defined by hyperglycemia caused by a lack of absolute or relative insulin efficiency in the human body. Diabetes prediction is one of the newest and fastest-growing technologies in medical data analysis. The clustering method for grouping diabetes data based on cluster head properties is the focus of this study. This study proposes a new BG prediction method called RNN, which is based on recurrent neural networks (RNN). The probability of values of the variable is calculated using the probability density function (PDF). For pre-processing and missing value analysis, we used an enhanced Decision Tree and a weighted K-means method. The proposed method uses the weighted Binary Bat Optimization algorithm for feature selection. In terms of diabetic categorization, numerical findings reveal that when compared to other existing methods, the DP-RNN approach with PDF produces the way PDF-DPRNN produces the more accurate classification result. Keywords: PDF, RNN, Decision Tree, Bat Optimization, Diabetes mellitus

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.

A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

A novel enhanced decision tree model for detecting chronic kidney disease

Prediction of diseases is sensitive as any error can result in the wrong person's treatment or not treating the right patient. Besides, some features distinguish a disease from curable to fatal or curable to chronic disease. Data mining techniques have been widely used in health-related research. The researchers, so far, could attain around 97 percent accuracy using several methods. Some researchers have demonstrated that the selection of correct features increases the prediction accuracy. This research work propose a method to distinguish between chronic and non-chronic kidney disease, identify its crucial features without reducing the accuracy of prediction, and a prediction algorithm to eliminate the possibility of under or overfitting. This study uses the recursive feature elimination (RFE) method that selects an optimal subset of features and an ensemble algorithm, the enhanced decision tree (EDT), to predict the disease. The results obtained in this paper show that the accuracy level of EDT is not changed with the removal of less significant features, thus enabling the decision-makers to concentrate on few features to reduce time and error of treatment. EDT establishes substantially high consistency in predicting, with or without feature selection, the disease.

Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach

Conventional optimization-driven secure relay selection relies on maximization algorithm and accurate channel state information (CSI) of both legitimate and eavesdropper channels. Particularly, estimating and collecting accurate eavesdropper CSI is a difficult task. In this paper, we exploit the benefits of machine learning in solving secure relay selection problem from a data-driven perspective. We convert secure relay selection to a multiclass-classification problem and solve it by a decision-tree-based scheme, which is composed of three phases - preparing training data, building decision tree and predicting relay selection. To meet decision tree’s requirement that input features must take discrete values, a feature extraction method is proposed to generate discrete input by quantizing the accurate CSI of legitimate and eavesdropper channels. By this means, the decision-tree-based relay selection only requires quantized CSI feedback which takes substantially fewer bits in predicting phase. For the purpose of optimizing quantization parameters and enhancing decision tree prediction, we further derive three splitting criteria, i.e. information gain, information gain ratio and Gini index. Simulation results show that if the quantization parameters are set properly, the proposed decision-tree-based scheme can achieve satisfactory performance in terms of average secrecy rate while reducing computational complexity and feedback amount.

Predicting Crisis in Global Trade Network: An Enhanced Decision Tree Based Methods

International Trade Relations represent a natural Social Information Network that has been extensively analyzed for various purposes like monitoring the global economy. The aim is to use the Global Trade Network to predict the occurrence of natural disasters or financial crisis based on the fact that the trade relations tax a hit in their patterns. The Global Network compromises of Export-Import Relations between the countries in the form of aWeighted Social Network. Predicting Trade relations help us effectively predict any future crisis and prepare for the same. An analysis of the Global Trade Network would discuss the centrality measures and Degree strengths. Using a list of crises which has occurred in the past and with the help of an efficient Machine Learning Model and Sampling Technique the aim is to improve the accuracy and precision of our prediction and discuss the implications on the network.

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.

PRM222 - STUDY OF THE SEVERITY OF VASO-OCCLUSIVE CRISIS FOR PATIENT SUFFERING FROM SICKLE CELL DISEASE IN FRANCE USING A MACHINE LEARNING APPROACH ON THE FRENCH MEDICAL INFORMATION SYSTEM DATABASE

The PMSI (medical information system database) is the French national administrative database containing medical information and patients’ features for millions of hospital stays. Most medical studies only use descriptive methods to analyze patient cohorts. The objective of this study is to assess the capability of an innovative Machine Learning algorithm to discover hidden values in health data and to bring a better understanding of diseases. We applied this approach to predict the severity of Vaso-Occlusive Crisis (VOC) for patients suffering from Sickle-Cell Disease (SCD). The severity is expressed by the frequency of VOC and the average time between two VOC. We only selected hospital stays with an SCD related ICD-10 code. Patients hospitalized with a VOC code in 2014 were extracted and followed up for two years. 20 groups of comorbidities, 7 features related to the patient’s medical history and 8 features about the patient’s life have been identified. Data were analyzed with an Enhanced Decision Tree technique, to explain the severity of VOC with non-linear combinations of these 35 variables. 5 630 patients were included in the study. The enhanced decision tree was able to gather patients into distinct groups based on patient and disease features. 20 distinct groups were identified. The most discriminating variables for the severity of VOC were the features related to patients’ history and the number of received transfusions during the follow-up, whereas the comorbidities, the age or the gender were not discriminating. This exploratory study shows that Enhanced Decision Trees are relevant to identify patient profiles from healthcare databases and have predictive capabilities. It helps identifying leverages to prevent the severity of VOC, and so to adapt the treatment given to patients according to the risk of having a VOC in the next months.

Implementation and Analysis of the Performance of EDTA (Enhanced Decision Tree Data Mining Algorithm) for diagnosis of Angioplasty and Stents for Heart Disease Treatment

Implementation and Analysis of the Performance of EDTA (Enhanced Decision Tree Data Mining Algorithm) for diagnosis of Angioplasty and Stents for Heart Disease Treatment

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.