A Two-Stage Classifier That Identifies Charge and Punishment under Criminal Law of Civil Law System

Thammaboosadee, Sotarat; Watanapa, Bunthit; Chan, Jonathan H.; Silparcha, Udom

doi:10.1587/transinf.e97.d.864

Cited by 5 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[44,45]. Moreover, this type of data is sometimes provided in the form of an XML (Extensible Markup Language) document [44,46,47] or electronic database [42,48,49]. Inherently, structured document-type data are a sort of table data.…”

Section: Input Data and Algorithmsmentioning

confidence: 99%

A Survey of Research on Data Analytics-Based Legal Tech

et al. 2021

View full text Add to dashboard Cite

Data analytics provides important tools and methods for processing the data generated during legal services. This paper aims to provide a systematic survey of the research papers on the application of quantitative data analytics algorithms in the legal domain. To this end, relevant research papers were collected and used to analyze topics and trends of research on data analytics-based Legal Tech. The key findings of this paper are as follows. Firstly, the number of research papers about Legal Tech has increased dramatically recently. Secondly, the application of supervised learning techniques to legal judgment data is a very popular approach in this research area. Thirdly, preprocessing legal documents is a very important procedure as many legal documents exist in text form. Fourthly, artificial neural networks and their variations are widely used in research on data analytics-based Legal Tech. Fifthly, data analytics-based Legal Tech is a multidisciplinary research topic related to computer science and social science, etc.

show abstract

Section: Input Data and Algorithmsmentioning

confidence: 99%

A Survey of Research on Data Analytics-Based Legal Tech

et al. 2021

View full text Add to dashboard Cite

show abstract

Weighted ReliefF with threshold constraints of feature selection for imbalanced data classification

Song

Dai

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

SummaryFeature selection is a useful method for fulfilling the data classification since the inherent heterogeneity of data and the redundancy of features are often encountered in the current data exploding era. Some commonly used feature selection algorithms, which include but are not limited to Pearson, maximal information coefficient, and ReliefF, are well‐posed under the assumption that instances are distributed homogenously in datasets. However, such an assumption might be not true in the practice. As such, in the presence of data imbalance, these traditional feature selection algorithms might be invalid due to their prejudices to the minority class, which includes few samples. The purpose of the addressed problem in this article is to develop an effective feature selection algorithm for imbalanced judicial datasets, which is capable of extracting essential features while deleting negligible ones according to the practical feature requirements. To achieve this goal, the number and the distribution of samples in each class are fully taken into consideration for the correlation analysis. Compared with the traditional feature selection algorithms, the proposed improved ReliefF algorithm is equipped with: (i) different weights of features according to the characteristics of heterogeneous samples in different classes; (ii) justice for imbalanced datasets; and (iii) threshold constraints resulting from the practical feature requirements. Finally, experiments on a judicial dataset and six public datasets well illustrate the effectiveness and the superiority of the proposed feature selection algorithm in improving the classification accuracy for imbalanced datasets.

show abstract