Predictive modeling in automotive direct marketing

Gersten, Wendy; Wirth, Rüdiger; Arndt, Dirk

doi:10.1145/347090.347174

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…-analysis of warranty claims at Daimler Chrysler (Hipp & Linder, 1999); -automotive direct marketing (Gersten et al, 2000); -analysis of data concerning construction of large and tall buildings (Moyle et al, 2002); -control and improvement of air quality in Taiwan with the goal of identifying national pollutant distribution, with data retrieved from 71 monitoring stations (Li & Shue, 2004); -development of new tinplate quality diagnostic models (De Abajo et al, 2004).…”

Section: Applications Of Kddm Modelsmentioning

confidence: 99%

A survey of Knowledge Discovery and Data Mining process models

Kurgan

Musilek

2006

The Knowledge Engineering Review

296

174

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Knowledge Discovery and Data Mining is a very dynamic research and development area that is reaching maturity. As such, it requires stable and well-defined foundations, which are well understood and popularized throughout the community. This survey presents a historical overview, description and future directions concerning a standard for a Knowledge Discovery and Data Mining process model. It presents a motivation for use and a comprehensive comparison of several leading process models, and discusses their applications to both academic and industrial problems. The main goal of this review is the consolidation of the research in this area. The survey also proposes to enhance existing models by embedding other current standards to enable automation and interoperability of the entire process.

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Section: Applications Of Kddm Modelsmentioning

confidence: 99%

A survey of Knowledge Discovery and Data Mining process models

Kurgan

Musilek

2006

The Knowledge Engineering Review

296

174

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“…depth of DT, pre-pruning and post-pruning rules, splitting method) with multiple options for each. Gersten et al (2000) notes that with regard to setting parameter values, there is "no practicable approach to select . .…”

Section: Discussionmentioning

confidence: 99%

Splitting methods for decision tree induction: An exploration of the relative performance of two entropy-based families

Osei-Bryson

Giles

2006

Inf Syst Front

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Decision tree (DT) induction is among the more popular of the data mining techniques. An important component of DT induction algorithms is the splitting method, with the most commonly used method being based on the Conditional Entropy (CE) family. However, it is well known that there is no single splitting method that will give the best performance for all problem instances. In this paper we explore the relative performance of the Conditional Entropy family and another family that is based on the Class-Attribute Mutual Information (CAMI) measure. Our results suggest that while some datasets are insensitive to the choice of splitting methods, other datasets are very sensitive to the choice of splitting methods. For example, some of the CAMI family methods may be more appropriate than the popular Gain Ratio (GR) method for datasets which have nominal predictor attributes, and are competitive with the GR method for those datasets where all predictor attributes are numeric. Given that it is never known beforehand which splitting method will lead to the best DT for a given dataset, and given the relatively good performance of the CAMI methods, it seems appropriate to suggest that splitting methods from the CAMI family should be included in data mining toolsets.

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“…The collection of semantic terms to build a taxonomy or an ontology may generally be pursued via supervised (requires manual labeling and training), reinforcement learning, such as bootstrapping or unsupervised learning (using automated classification procedures) [3], [39], [40]. Relevant text clusters for semantic enrichment may be identified by using mathematical algorithms, such as k-means [41] or maximum entropy models [42], [43].…”

Section: Background and Related Workmentioning

confidence: 99%

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

et al. 2019

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To gain a comprehensive overview of new scientific findings with the enormous, ever-increasing amount of published information, we apply a new combinatorial approach that complements the process of reading scientific articles by supplementing artificial intelligence technologies. We present a combinatorial approach, which we illustrate in the form of a ''double funnel of artificial intelligence.'' Our approach suggests to largely increase the amount of data at the beginning of the data collection process and to subsequently clean and enrich the data set in order to gain much more knowledge at the end of the procedure compared to a ''classical'' literature review. We use natural language processing and text visualization techniques to uncover findings that are generally unbeknown to the human reader due to the inability to process very large amounts of text. By illustrating the individual steps using practical examples taken from use cases, we demonstrate the merits of our approach. With our methodology, we are able to reproduce findings from ''regular'' review papers; however, we discover additional and new findings in different fields, such as data science or medicine. We also point out the limitations of our approach. Finally, we make suggestions as to how the methodology could be further developed. INDEX TERMS Computational and artificial intelligence, document handling, fuzzy control, knowledge acquisition, pattern analysis, scientific publishing, text mining, text processing.

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Predictive modeling in automotive direct marketing

Cited by 19 publications

References 6 publications

A survey of Knowledge Discovery and Data Mining process models

A survey of Knowledge Discovery and Data Mining process models

Splitting methods for decision tree induction: An exploration of the relative performance of two entropy-based families

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

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