Distributed Communication Decision Tree Algorithm for Disseminated and Heterogeneous Environment

2011 ◽  
Vol 403-408 ◽  
pp. 1002-1007
Author(s):  
Chandra Chandra ◽  
P. Ajitha
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Communication Cost ◽  
Classification Algorithms ◽  
Heterogeneous Environment ◽  
Decision Tree Algorithm ◽  
Distributed Environment ◽  
Tree Algorithm ◽  
Current Classification ◽  
Distributed Communication

Current Classification algorithms require large amounts of data to be stored enduringly in the memory for long assortment and amount of time. Diverse classification techniques had been already proposed in the literature for both in the run of the mill environment and distributed environment. Mining of decision trees in the distributed environment can be able to handle the large amount of data but with high communication cost. A new distributed communication decision tree algorithm is proposed here which reduces the communication cost for the transmission of the data in the distributed and heterogeneous environment.

scholarly journals An Explainable Bayesian Decision Tree Algorithm

2021 ◽  
Vol 7 ◽  
Author(s):  
Giuseppe Nuti ◽  
Lluís Antoni Jiménez Rugama ◽  
Andreea-Ingrid Cross
Keyword(s):  
Monte Carlo ◽  
Decision Tree ◽  
Decision Trees ◽  
Medical Industry ◽  
Data Sets ◽  
Bayesian Decision ◽  
Decision Tree Algorithm ◽  
Probabilistic Framework ◽  
Classification Problems ◽  
Tree Algorithm

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we provide a deterministic Bayesian Decision Tree algorithm that eliminates the sampling and does not require a pruning step. This algorithm generates the greedy-modal tree (GMT) which is applicable to both regression and classification problems. We tested the algorithm on various benchmark classification data sets and obtained similar accuracies to other known techniques. Furthermore, we show that we can statistically analyze how was the GMT derived from the data and demonstrate this analysis with a financial example. Notably, the GMT allows for a technique that provides explainable simpler models which is often a prerequisite for applications in finance or the medical industry.

scholarly journals Innovations in Financial Management: Recursive Prediction Model Based on Decision Trees

2020 ◽  
pp. 276-292
Author(s):  
Ivana Podhorska ◽  
Jaromir Vrbka ◽  
George Lazaroiu ◽  
Maria Kovacova
Keyword(s):  
Decision Tree ◽  
Emerging Markets ◽  
Prediction Model ◽  
Decision Trees ◽  
Financial Distress ◽  
Financial Management ◽  
Test Sample ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
The Creation

The issue of enterprise financial distress represents the actual and interdisciplinary topic for the economic community. The bankrupt is thus one of the major externalities of today’s modern economies, which cannot be avoided even with every effort. Where there are investment opportunities, there are individuals and businesses that are willing to assume their financial obligations and the resulting risks to maintain and develop their standard of living or their economic activities. The decision tree algorithm is one of the most intuitive methods of data mining that can be used for financial distress prediction. Systematization literary sources and approaches prove that decision trees represent the part of the innovations in financial management. The main propose of the research is a possibility of application of a decision tree algorithm for the creation of the prediction model, which can be used in economy practice. The Paper's main aim is to create a comprehensive prediction model of enterprise financial distress based on decision trees, under the conditions of emerging markets. Paper methods are based on the decision tree, with emphasis on algorithm CART. Emerging markets included 17 countries: Slovak Republic, Czech Republic, Poland, Hungary, Romania, Bulgaria, Lithuania, Latvia, Estonia, Slovenia, Croatia, Serbia, Russia, Ukraine, Belarus, Montenegro, and Macedonia. Paper research is focused on the possibilities of implementation of a decision tree algorithm for the creation of a prediction model in the condition of emerging markets. Used data contained 2,359,731 enterprises from emerging markets (30% of total amount); divided into prosperous enterprises (1,802,027) and non-prosperous enterprises (557,704); obtained from Amadeus database. Input variables for the model represented 24 financial indicators, 3 dummy variables, and the countries' GDP data, in the years 2015 and 2016. The 80% of enterprises represented the training sample and 20% test sample, for model creation. The model correctly classified 93.2% of enterprises from both the training and test sample. Correctly classification of non-prosperous enterprises was 83.5% in both samples. The result of the research brings a new model for the identification of bankrupt enterprises. The created prediction model can be considered sufficiently suitable for classifying enterprises in emerging markets. Keywords prediction model, decision tree, emerging markets.

scholarly journals Classification Based on Decision Tree Algorithm for Machine Learning

2021 ◽  
Vol 2 (01) ◽  
pp. 20-28
Author(s):  
Bahzad Charbuty ◽  
Adnan Abdulazeez
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Decision Tree ◽  
Decision Trees ◽  
Text Classification ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Different Types ◽  
Classification Images ◽  
Disease Analysis

Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.

scholarly journals To Identify the Improvement Pattern of Self-financing Arts and Science College Student’s Academic Performance using Classification Algorithms

2019 ◽  
Vol 8 (2) ◽  
pp. 2429-2433
Keyword(s):  
Academic Performance ◽  
Decision Tree ◽  
Naive Bayes ◽  
Research Work ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Science College ◽  
Bayes Algorithm

The aim of this research work is to identify the improvement pattern of academic performance of final year students of self-financing arts and science colleges. The data was collected from the students of nine Arts and Science Colleges. The data contains demographic, socio-economic, residence and college location, subjects, infrastructural facilities, faculty concern and self-motivation attributes. The classification algorithms like Naïve Bayes, Decision tree and CBPANN are applied on the student’s data. The outcome of the research can be used to improve the academic performance students studying in self-financing arts and science colleges located in educationally backward areas. The experiment results shows that the accuracy value for Naïve Bayes algorithm is 92.63%, accuracy value for Decision Tree algorithm is 96.41% and accuracy value for CBPANN algorithm is 99.49%

scholarly journals Enhanced Decision Tree Algorithm for Discovering Intra and Inter Class Exceptions

2019 ◽  
Vol 8 (11) ◽  
pp. 1539-1548
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Algorithms ◽  
Decision Tree Algorithm ◽  
High Confidence ◽  
Tree Algorithm ◽  
General Rules ◽  
Tree Algorithms ◽  
Small Disjuncts

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

scholarly journals Performance Analysis of Classification Algorithms for Fault Diagnosis in Rotating Machines

2020 ◽  
Vol 9 (6) ◽  
pp. 452-455
Keyword(s):  
Decision Tree ◽  
Fuzzy Inference ◽  
Probabilistic Neural Network ◽  
Vibration Signal ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Decision Tree Algorithm ◽  
Rotating Machines ◽  
Inference System ◽  
Tree Algorithm

Classification of any given vibration signal as healthy or faulty can be done by employing classification algorithms available to us. Identification of a fitting classification algorithm is a task that should be done at the time of identification of the problem statement itself, such that required changes can be done in it if the need be. Hilbert Huang Transform (HHT) empowered Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to obtain the most significant features of the vibration signals of both healthy and faulty rotating machines in the time and frequency domain, namely RMS velocity, Kurtosis, and Crest Factor (RKC). They were then fed to classification algorithms to classify the machines as healthy or faulty. Five machine learning techniques such as Probabilistic Neural Network (PNN), decision tree (DT), k- nearest neighbour (KNN), and Radial Basis Network (RBN) are utilized as classification algorithms. Decision Tree algorithm was found to be the optimal classification technique; overfitting was found to be a notable issue. To improve prediction, the decision tree algorithm was parallelly ensembled into Random Forest using the Bootstrap Aggregation method.

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