Fast Text Comparison Based on ElasticSearch and Dynamic Programming

Xiao, Pengcheng; Lu, Peng; Luo, Chunqi; Zhu, Zhousen; Liao, Xuehua

doi:10.1007/978-981-99-7254-8_5

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…Once all energy levels are computed the feature with minimum value is considered the best attribute for split. In the future works, it is proposed to apply in different applications such as page ranking [13], communication theory [3], , analysing epidemic datasets i.e., COVID-19 [14], image text classification [15], biomedical applications [16] [17], price monitoring [18], sentiment classification [19], text comparison [20] etc.…”

Section: Discussionmentioning

confidence: 99%

A Novel Statistical Theoretical Split Metric for Decision Tree Classification

Biswas

2024

Advancements in Communication and Systems

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Decision trees (DTs) are a significant category of logical tools in machine learning (ML), used to classify both text and numerical data. Over the years, two primary criteria for splitting DTs have been prevalent: information gain, which hinges on Shannon’s entropy, and the Gini index. Both these criteria rely on the empirical probabilities of classes within the attribute space of the dataset. In this study, a novel split criteria is introduced, rooted in the principles of statistical mechanics. This measure draws inspiration from the second law of Thermodynamics, which stipulates that in a closed system with unchanging external conditions and entropy, the internal energy of the system will decrease and reach a minimum at equilibrium. This novel split criterion was tested on four datasets, each containing at least 100 instances. The results demonstrated a comprehensive enhancement in accuracy, precision, recall, and F1-score.

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Section: Discussionmentioning

confidence: 99%

A Novel Statistical Theoretical Split Metric for Decision Tree Classification

Biswas

2024

Advancements in Communication and Systems

View full text Add to dashboard Cite

show abstract

Fast Text Comparison Based on ElasticSearch and Dynamic Programming

Cited by 1 publication

References 31 publications

A Novel Statistical Theoretical Split Metric for Decision Tree Classification

A Novel Statistical Theoretical Split Metric for Decision Tree Classification

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