Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Decision Tree Model Based Gene Selection and Classification for Breast Cancer Risk Prediction

Abstract: Breast cancer is considered the most frequently diagnosed cancer in worldwide women and ranked second after lung cancer. Early diagnosis of this cancer may increase the chance to get an early treatment, which can increase the chance of survival for women suffering from this disease. Recently, Microarray data technology has brought a great opportunity to make diagnose cancer faster and easy. However, the most common challenge of gene expression data is high dimensionality, i.e., thousands of genes, and a few tens of patients, which makes any prediction approach difficult to apply. To take this challenge, a C5.0 based feature selection approach is being proposed. The strongest point of our approach resides in the combination of two feature selection techniques: the fisher-score based filter method and the inner feature selection ability of C5.0. The classification algorithms used to assess our approach in terms of prediction accuracy are Artificial neural Networks, C5.0 Decision Tree, Logistic Regression, and Support Vector Machine. Compared to the state-of-the-art models, our approach can predict breast cancer with the highest accuracy based on a strict minimum of genes.

Introduction to ethnographic decision tree modeling

Abstract: With rapid advances in healthcare technology, patients are increasingly compelled to make medical decisions in unfamiliar and highly stressful situations Nurses, therefore, have a responsibility to understand the medical options, and consequences of each, in order to help patients make the best decisions for themselves Ethnographic decision tree modeling is a research "triangulation" tool conducted in two stages The first stage uses ethnographic fieldwork techniques to determine the criteria used by insiders (ie, decision makers) to make a real-world decision These criteria are combined into a decision tree The second stage applies a quantitative research method to verify the degree of predictive accuracy of the decision tree This method addresses the meaning and predictability of decision-making This paper introduces ethnographic decision tree modeling and provides a perspective on inquiring about the bases of patient decision-making

Dynamic databases with optimal in order time complexity

Abstract: Abstract We investigate the complexity of basic operations over dynamic databases including search, insertion, and deletion of records. We suggest a structure of a database and corresponding algorithms which with the use of two parallel computing processes permit to fulfil these operations over a database in a constant time on the average and with logarithmic complexity in the worst case.

The Accuracy of Decision Tree Induction in a Noisy Domain for Expert Systems Construction

Abstract: Many studies have shown that decision tree induction methods could be used to determine rules for expert systems. Pruning techniques are often used to increase the accuracy of an induced decision tree over the instance space. While recent results of decision tree induction show that large samples may be required to induce a decision tree of small error, recent expository studies have used very small sample sizes. In such cases it is of value to obtain a posterior evaluation of the error of the induced concept. In this paper we give three methods to estimate the accuracy of a pruned decision tree. The first method assumes uniform prior distribution. For those cases where uniform prior is not appropriate, we develop a method to obtain appropriate prior using a beta distribution. Finally, we provide a general bound which requires no assumption over the instance space. These results can be used when a pruned decision tree is used to classify the original domain or another close domain.

A clustering-based decision tree induction algorithm

Abstract: Decision tree induction algorithms are well known techniques for assigning objects to predefined categories in a transparent fashion. Most decision tree induction algorithms rely on a greedy top-down recursive strategy for growing the tree, and pruning techniques to avoid overfitting. Even though such a strategy has been quite successful in many problems, it falls short in several others. For instance, there are cases in which the hyper-rectangular surfaces generated by these algorithms can only map the problem description after several sub-sequential partitions, which results in a large and incomprehensible tree. Hence, we propose a new decision tree induction algorithm based on clustering which seeks to provide more accurate models and/or shorter descriptions more comprehensible for the end-user. We do not base our performance analysis solely on the straightforward comparison of our proposed algorithm to baseline methods. Instead, we propose a data-dependent analysis in order to look for evidences which may explain in which situations our algorithm outperforms a well-known decision tree induction algorithm.