A probabilistic framework for dynamic k estimation in kNN classifiers with certainty factor

Bhattacharya, Gautam; Ghosh, Koushik; Chowdhury, Ananda S.

doi:10.1109/icapr.2015.7050683

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…Eunomos is a legal information system which has been developed as a result of the ICT4LAW project [28]. It is intended to become a full-fledge legal document management system for legal firms, legal practitioners and legal scholars [29,30].…”

Section: Literature Reviewmentioning

confidence: 99%

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

Pudaruth

Soyjaudah

Gunputh

2017

Complex Intell. Syst.

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The classification of legal documents has been receiving considerate attention over the last few years. This is mainly because of the over-increasing amount of legal information that is being produced on a daily basis in the courts of law. In the Republic of Mauritius alone, a total of 141,164 cases were lodged in the different courts in the year 2015. The Judiciary of Mauritius is becoming more efficient due to a number of measures which were implemented and the number of cases disposed of in each year has also risen significantly; however, this is still not enough to catch up with the increase in the number of new cases that are lodged. In this paper, we used the k-nearest neighbour machine learning classifier in a novel way. Unlike news article, judgments are complex documents which usually span several pages and contains a variety of information about a case. Our approach consists of splitting the documents into equal-sized segments. Each segment is then classified independently of the others. The selection of the predicted category is then done through a plurality voting procedure. Using this novel approach, we have been able to classify law cases with an accuracy of over 83.5%, which is 10.5% higher than when using the whole documents dataset. To the best of our knowledge, this type of process has never been used earlier to categorise legal judgments or other types of documents. In this work, we also

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Section: Literature Reviewmentioning

confidence: 99%

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

Pudaruth

Soyjaudah

Gunputh

2017

Complex Intell. Syst.

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“…The first matter is that KNN classification performance is affected by existing outliers, especially in small training sample-size situations [22]. This implies that one has to pay attention in selecting a suitable value for neighborhood size k [23]. Firstly, to overcome the influence of outliers, a local mean-based k nearest neighbor (LMKNN) classifier has been introduced in [3].…”

Section: Introductionmentioning

confidence: 99%

A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance

et al. 2018

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The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each class are found which are used to find k different local mean vectors, and then employed to compute their harmonic mean distance to the query sample. Lastly, the query sample is assigned to the class with minimum harmonic mean distance. The experimental results based on twenty-six real-world datasets shows that the proposed LMKHNCN classifier achieves lower error rates, particularly in small sample-size situations, and that it is less sensitive to parameter k when compared to therelated four KNN-based classifiers.

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A parameter-free KNN for rating prediction

Fopa

Ndiaye

et al. 2022

Data & Knowledge Engineering

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A probabilistic framework for dynamic k estimation in kNN classifiers with certainty factor

Cited by 10 publications

References 23 publications

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

An innovative multi-segment strategy for the classification of legal judgments using the k-nearest neighbour classifier

A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance

A parameter-free KNN for rating prediction

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