A System for Unstructured Data Mining using Dynamic Ensemble Selection

被引：0

作者：

Calado, Raquel Bezerra ^{[1
]}

Rodriguez Torres, Leandro Sigfredo ^{[2
]}

Maciel, Alexandre M. A. ^{[1
]}

机构：

[1] Univ Pernambuco, Recife, PE, Brazil

[2] Kurier Inteligencia Juridica, Recife, PE, Brazil

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2020年

关键词：

Unstructured Data; Text Mining; Dynamic Ensemble Selection; CLASSIFIER SELECTION; COMPETENCE; FRAMEWORK;

D O I：

10.1109/smc42975.2020.9282967

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Unstructured data represent as much as 90% of all business-relevant information. In Brazil, the practice of printing official journals dates back to the 19th century. Today more than 200 official journals in circulation, which together accumulate around 1.4 billion publications without textual standard. This work proposes the development of a system for unstructured data mining using a Dynamic Ensemble Selection. JudEasy implements, added in addition to classic text pre-processing methods, a set of twelve DES and a static method for creating categorized textual models for Brazilian of official journals. As results the DES-KL model obtained the highest accuracy rate of 96.81% and exceptional precision of 0.99.

引用

页码：1988 / 1993

页数：6

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