Towards a Synthetic Data Generator for Matching Decision Trees

Abstract

Abstract:It is popular to use real-world data to evaluate or teach data mining techniques. However, there are some disadvantages to use real-world data for such purposes. Firstly, real-world data in most domains is difficult to obtain for several reasons, such as budget, technical or ethical. Secondly, the use of many of the realworld data is restricted or in the case of data mining, those data sets do either not contain specific patterns that are easy to mine for teaching purposes or the data needs special preparation and the algorithm needs very specific settings in order to find patterns in it. The solution to this could be the generation of synthetic, "meaningful data" (data with intrinsic patterns). This paper presents a framework for such a data generator, which is able to generate datasets with intrinsic patterns, such as decision trees. A preliminary run of the prototype proves that the generation of such "meaningful data" is possible. Also the proposed approach could be extended to a further development for generating synthetic data with other intrinsic patterns.

Fuzzy Logic and Deep Learning Integration in Likert Type Data

“…In other words, data sets do not contain purposeful models, or it requires particular preparation to find the pattern inside. Obtaining experimental results by producing artificial data or in other words synthetic data can overcome these disadvantages (Peng and Hanke 2016).…”

SysGpr: Sistema de generación de señales sintéticas pseudo-realistas

“…En (Peng and Hanke, 2016) los autores generan nuevos conjuntos de datos sintéticos por medio deárboles de decisión mediante una modificación del algoritmo ID3 (Iterative Dichotomiser 3). Mediante el uso de losárboles de decisión los autores consiguen crear interdependencia entre los datos de los conjuntos de datos generados con la intención de obtener conjuntos de datos genéricos con los que testear cualquier aplicación de aprendizaje automático.…”

“…Los datos sintéticos como herramienta para poner a prueba métodos y modelos desarrollados se utilizan en diversos campos científicos, tales como reconocimiento y generación de patrones (Jiang et al, 2009), minería de datos (Peng and Hanke, 2016), en aprendizaje automático (Ekbatani et al, 2017), etc.…”