Analyzing consumer behavior is a costly implementation of sophisticated information technology, which requires detailed planning and business knowledge for successful adoption. The current trend on consumer behavior analysis has been recognized on the business problem rather than on the information technology. This study presents a two-stage framework of consumer behavior analysis, and the key feature is a cascade involving self-organizing map (SOM) neural network to divide customers into homogeneous groups of customers and a decision-tree simplified method to identify relevant knowledge. Identifying consumers by this approach is helpful characteristic of customers and facilitates marketing strategy development.

Enhancing Consumer Behavior Analysis by Data Mining Techniques

The complexity of liver makes it easily affected by disease of disorder. So diagnosing liver disorder disease is a high interest to data miners, and decision trees have been useful data mining tools to diagnose the disease, but the accuracy of decision trees has been limited due to insufficient data. In order to generate more accurate decision trees for liver disorder disease this paper suggests a method based on over-sampling in minor classes to compensate the insufficiency of data effectively. Experiments were done with two representative algorithms of decision trees, C4.5 and CART, and a data set, 'BUPA liver disorder', and showed the validity of the method.

Improving the prediction accuracy of liver disorder disease with oversampling

The purpose of this study is to identify the relationship between weather variables and buildings damaged in natural disasters. We used four datasets on building damage history and 33 weather datasets from 230 regions in South Korea in a decision tree analysis to evaluate the risk of building damage. We generated the decision tree model to determine the risk of rain, gale, and typhoon (excluding gale with less damage). Using the weight and limit values of the weather variables derived using the decision tree model, the risk of building damage was assessed for 230 regions in South Korea until 2100. The number of regions at risk of rain damage increased by more than 30% on average. Conversely, regions at risk of damage from snowfall decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, while those at high risk of the same increased by up to 62.5% under RCP 8.5. The results of this study are highly fluid since they are based on the uncertainty of future climate change. However, the study is meaningful because it suggests a new method for assessing disaster risk using weather indices.

Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

In recent work, we developed a classifier inspired by reliability engineering, in which the boundaries between classes was constrained based on parameterized formulae involving single feature probabilities. This produced competitive results on the 'Iris Flower' dataset, however it was not suitable for learning tasks with highly nonlinear class boundaries (where class membership correlates very poorly with any individual feature value). The 'Balance Scale' dataset is an example of the latter type of dataset, where individual features can only influence collectively on the decision of class membership. Keeping this in mind, in this paper we describe a new nonlinear discriminant classifier, in which an evolutionary algorithm learns a probabilistic model based on a constrained, parameterized combination of all feature values. We test this method on both the 'Iris Flower' and 'Balance Scale' datasets. The results are highly competitive.

A Novel Nonlinear Discriminant Classifier Trained by an Evolutionary Algorithm

In reliability engineering, the reliability of a system is estimated by considering the dependencies between the system's components The probability of a system failure is then expressed in terms of the states of its components Meanwhile, in some machine learning approaches, the probability of class membership is expressed in terms of the values (which can be seen as ‘states’) of various features (which can be seen as ‘components’) In this paper, we explore this analogy further to develop a classification algorithm in which the decision for class membership is based on specific combinations of feature states, where those combinations are inspired by reliability engineering In essence, our classification model considers the features to be comp onents arranged in a parallel in a system, while different classes represent different system configurations We describe the approach, present initial promising results, and speculate on further development of the approach

Use of reliability engineering concepts in machine learning for classification

To avoid checking unnecessary or irrelevant conditions of rules, the irrelevant values problem of the decision tree is addressed. We propose an algorithm to remove irrelevant conditions of rules in the process of converting the decision tree to rules according to the semantics of the decision tree. Since our algorithm depends only on the semantics of the decision tree, our algorithm can be integrated into any existing tree-construction algorithm with negligible increase in computational cost concerning that of constructing the decision tree. Moreover, as a side effect, the resultant rules are less likely to suffer from missing branches problem.

Rules Generation from the Decision Tree

When a decision tree is represented by a collection of rules, the antecedents of individual rules may contain irrelevant conditions To avoid generating rules with irrelevant conditions, we propose a new algorithm to remove irrelevant conditions of rules in the process of converting the decision tree to rules according to information on the decision tree Since irrelevant conditions are removed from the resultant rules, the resultant rules are more general than those represented by the decision tree As a side effect, the resultant rules are less likely to suffer from missing branches

The irrelevant values problem in the id3 tree

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Rules Generation from the Decision Tree

The irrelevant values problem in the id3 tree