Constrained cascade generalization of decision trees

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding. Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE. Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking. The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools. To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019. In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging. Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.

Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Such datasets are particularly challenging to build classifiers for, due to their very high dimensional nature and small sample size. Decision trees are a seemingly attractive technique for this domain, due to their easily interpretable white box nature and noise resistance. However, existing decision tree methods tend to perform rather poorly for classifying gene expression data. To address this gap, we introduce a new technique for building decision trees that is better suited to this scenario. Our method is based on consideration of the area under the Receiver Operating Characteristics (ROC) curve, to help determine decision tree characteristics, such as node selection and stopping criteria. We experimentally compare our algorithm, called ROC-tree, against other well known decision tree techniques, on a number of gene expression datasets. The experimental results clearly demonstrate that ROC-tree can deliver better classification accuracy in a range of challenging situations.

Post-pruning in decision tree induction using multiple performance measures

Corrigendum to ‘Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars'

The simplicity and interpretability of decision tree induction makes it one of the more widely used machine learning methods for data classification. However, for continuous valued (real and integer) attribute data, there is room for further improvement in classification accuracy, complexity, and tree scale. We propose a new K-ary partition discretization method with no more than K−1 cut points based on Gaussian membership functions and the expected class number. A new K-ary crisp decision tree induction is also proposed for continuous valued attributes with a Gini index, combining the proposed discretization method. Experimental results and non-parametric statistical tests on 19 real-world datasets showed that the proposed algorithm outperforms four conventional approaches in terms of both classification accuracy, tree scale, and particularly tree depth. Considering the number of nodes, the proposed methods decision tree tends to be more balanced than in the other four methods. The complexity of the proposed algorithm was relatively low.

C4.5 algorithm is used to simplify decision tree from decision table by generating decision tree from an existing decision tree. With this algorithm, knowledge base in the decision table can be simplified. This research will build a consultation program using C4.5 algorithm which is called decision tree generator. Decision tree generator provides inference facility and a user interface for consultation. The user is required to build a knowledge base first, and the application will generate user interface automatically. There are two steps in decision tree generator: firstly, the application will build the decision tree, and after that the application will build the user interface for consultation session. The results of this research show that the decision tree generator can get goal and advice from tree exploration in consultation session.

Decision tree induction is one of the widely used classification approaches. It constructs a tree in which an internal node is split based on values of a selected attribute. Depending on the attribute selected at each level of the tree, a training dataset could lead to many different trees. Consequently, it is possible that an unseen case is classified into different and conflict classes using different trees. On the other band, recently developed association role based classifications are able to generate more interesting and useful rules than decision trees. However, the large number of rules without appropriate data structure brings the issue of efficiency. In this paper, we propose to unify decision tree classification with association-based classification using generalized decision trees (GDT). GDT generalizes the concept of decision tree to encode all interesting classification rules discovered based on association in a tree form. it inherits the merits of both approaches and removes their drawbacks. The data structure and related algorithms are discussed. Results of an experimental study are presented to indicate the advantages of GDT.

Induction of decision trees as classification models through metaheuristics

Decision tree induction has been widely used to generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized in advance or during the learning process. The commonly used method is to partition the attribute range into two or several intervals using single or a set of cut points. One inherent disadvantage in these methods is that the use of sharp cut points makes the induced decision trees sensitive to noise. To overcome this problem this paper presents an alternative method called adaptive discretization based on Ant Colony Decision Tree (ACDT) approach. Experimental results showed that, by using that methodology, better classification accuracy has been obtained in both training and testing data sets in majority of cases concerning the classical decision tree constructed by ants. It suggests that the robustness of decision trees could be improved by means of this approach.

Healthy or Sick? Coevolution of Health Care and Public Health in a Comparative PerspectiveTrein, PhilippCambridge, Cambridge University Press (2018), 332 p., ISBN 978‐1108426497

Inducing decision trees with an ant colony optimization algorithm

Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation

In the last two decades, computational enhancements highly contributed to the increase in popularity of DTI algorithms. This cause the successful use of Decision Tree Induction (DTI) using recursive partitioning algorithms in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition, to name only a few. But recursive partitioning and DTI are two faces of

The COVID-19 pandemic has necessitated the development of accurate and efficient classification models for diagnosis and prognosis. While deep learning has shown promising results in various medical applications, its combination with ensemble methods, which amalgamate the predictions of multiple classifiers, can further enhance the model's accuracy. This research paper introduces a novel approach called Deep Neural Ensemble Classification (DNEC) to tackle the challenge of Developing an enhanced ensemble model using deep learning algorithms and comparing its performance with existing ensemble methods. The research problem stems from the literature gap, where existing studies primarily focus on single-model approaches, lacking in-depth exploration of ensemble methods for COVID-19 classification. Motivated by the potential improvement in classification accuracy through ensemble methods, this study aims to create a deep neural ensemble classification model to improve the classification accuracy tailored for COVID-19 data. A set of diverse classifiers, including k-nearest neighbour (IBK), decision tree (J48), naïve bayes (NB), support vector machine (SVM), and sequential minimal optimization (SMO), are utilized in the proposed ensemble method. The accuracy improvement of the ensemble classifiers is evaluated using various metrics such as precision, recall, F1 score, confusion matrix, and processing time. The proposed method demonstrates that IBK+SVM+NB emerges as the top-performing deep neural ensemble classifier with an accuracy score of 99.29% and a total run time of 27.61 seconds. The innovative ensemble techniques introduced in this research contribute to the existing body of knowledge by filling the identified literature gap and offering a novel and highly accurate approach for COVID-19 classification.