Title

Optimizing the Efficiency of Machine Learning Techniques

Document Type

Conference Proceeding

Source of Publication

Communications in Computer and Information Science

Publication Date

1-1-2020

Abstract

© 2020, Springer Nature Singapore Pte Ltd. The prediction of judicial decisions based on historical datasets in the legal domain is a challenging task. To answer the question about how the court will render a decision in a particular case has remained an important issue. Prior studies conducted on the prediction of judicial case decisions have datasets with limited size by experimenting less efficient set of predictors variables applied to different machine learning classifiers. In this work, we investigate and apply more efficient sets of predictors variables with a machine learning classifier over a large size legal dataset for court judgment prediction. Experimental results are encouraging and depict that incorporation of feature selection technique has significantly improved the performance of predictive classifier.

ISBN

9789811575297

ISSN

1865-0929

Publisher

Springer

Volume

1210 CCIS

First Page

553

Last Page

567

Disciplines

Computer Sciences

Keywords

Feature selection, Judicial case decisions, Machine learning, Random forest, Statistical test

Scopus ID

85090024958

Indexed in Scopus

yes

Open Access

no

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