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The number of clusters in hybrid predictive models: does it really matter?

100%
Łapczyński M., Jefmański B.
Przegląd Statystyczny
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2019
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vol. 66
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issue 3
228-238
EN
For quite a long time, research studies have attempted to combine various analytical tools to build predictive models. It is possible to combine tools of the same type (ensemble models, committees) or tools of different types (hybrid models). Hybrid models are used in such areas as customer relationship management (CRM), web usage mining, medical sciences, petroleum geology and anomaly detection in computer networks. Our hybrid model was created as a sequential combination of a cluster analysis and decision trees. In the first step of the procedure, objects were grouped into clusters using the k-means algorithm. The second step involved building a decision tree model with a new independent variable that indicated which cluster the objects belonged to. The analysis was based on 14 data sets collected from publicly accessible repositories. The performance of the models was assessed with the use of measures derived from the confusion matrix, including the accuracy, precision, recall, F-measure, and the lift in the first and second decile. We tried to find a relationship between the number of clusters and the quality of hybrid predictive models. According to our knowledge, similar studies have not been conducted yet. Our research demonstrates that in some cases building hybrid models can improve the performance of predictive models. It turned out that the models with the highest performance measures require building a relatively large number of clusters (from 9 to 15).
2

Number of Clusters and the Quality of Hybrid Predictive Models in Analytical CRM

100%
Łapczyński M., Jefmański B.
Studies in Logic, Grammar and Rhetoric
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2014
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vol. 37
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issue 1
141-157
EN
Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company (acquisition models), customers who purchase additional products (cross- and up-sell models) or customers intending to resign from the cooperation (churn models). During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive model combining decision trees (C&RT algorithm) and cluster analysis (k-means). During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality.
3
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Using Decision Trees to Evaluate Courier Companies from the Perspective of Online Retailers

100%
Kulińska E., Wojtynek L., Dendera-Gruszka M., Flauder S.
Logistics and Transport
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2017
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vol. 35
59-66
EN
The analysis of the research problem started from listing the issues of decision theory and the decision-making process that support the process of building decision trees. The area in question covers a procedure for building and proceeding when creating decision trees. The solution to the research problem consists in defining decision variables and arranging them into logical statements by writing down all possible variants and only accounting for the true ones. True solutions derived from coding were detailed and the number of occurring decision trees was calculated in the case under consideration. The decision problem was presented in the form of decision trees, which made it possible to select the optimum decision tree. The obtained results were considered and the optimum decision tree was chosen. At the same time, the record of decision variables was analyzed, providing the answer as to which courier company will best meet expectations of entrepreneurs and ensure the most satisfying cooperation. That company turned out to be K-EX. The article aimed to select a courier company from the perspective of online retailers, with the selection having been made using the method of decision trees based on four basic criteria defined within the research.
4
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Decision tree based model of business failure prediction for Polish companies

88%
Durica M., Frnda J., Svabova L.
Oeconomia Copernicana
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2019
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vol. 10
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issue 3
453-469
EN
Research background: The issue of predicting the financial situation of companies is a relatively young field of economic research. Its origin dates back to the 30's of the 20th century, but constant research in this area proves the currentness of this topic even today. The issue of predicting the financial situation of a company is up to date not only for the company itself, but also for all stakeholders. Purpose of the article: The main purpose of this study is to create new prediction models by using the method of decision trees, in achieving sufficient prediction power of the generated model with a large database of real data on Polish companies obtained from the Amadeus database. Methods: As a result of the development of artificial intelligence, new methods for predicting financial failure of the company have been introduced into financial prediction analysis. One of the most widely used data mining techniques in this field is the method of decision trees. In the paper, we applied the CART and CHAID approach to create a model of predicting the financial difficulties of Polish companies. Findings & Value added: For the creation of the prediction model, a total of 37 financial and economic indicators of Polish companies were used. The resulting decision trees based prediction models for Polish companies reach a prediction power of more than 98%. The success of the classification for non-prosperous companies is more than 83%. The created decision tree-based prediction models are useful mainly for predicting the financial difficulties of Polish companies, but can also be used for companies in another country.
5

E-Commerce Customers’ Preference Implicit Identification

88%
Zdziebko T.
Folia Oeconomica Stetinensia
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2012
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vol. 11
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issue 1
33-46
EN
Knowledge of users’ preferences are of high value for every e-commerce website. It can be used to improve customers’ loyalty by presenting personalized products’ recommendations. A user’s interest in a particular product can be estimated by observing his or her behaviors. Implicit methods are less accurate than the explicit ones, but implicit observation is done without interruption of having to give ratings for viewed items. This article presents results of e-commerce customers’ preference identification study. During the study the author’s extension for FireFox browser was used to collect participants’ behavior and preference data. Based on them over thirty implicit indicators were calculated. As a final result the decision tree model for prediction of e-customer products preference was build.
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