Different Classification Technique for Data mining in Insurance Industry using weka

Farooqui, Mohd Ahtesham; Sheetlani, Jitendra

doi:10.9790/0661-1901011118

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…DT uses a set IF-ELSE rules to do decision making. Farooqui et al [20] clearly stated that DT's performance is much higher when the classification involved decision making. In our task, each individual word in a name has a weighted influence towards one gender.…”

Section: ) Decision Tree (Dt)mentioning

confidence: 99%

Gender Prediction Based on Vietnamese Names with Machine Learning Techniques

Nguyen

et al. 2020

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

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As biological gender is one of the aspects of presenting individual human, much work has been done on gender classification based on people names. The proposals for English and Chinese languages are tremendous; still, there have been few works done for Vietnamese so far. We propose a new dataset for gender prediction based on Vietnamese names. This dataset comprises over 26,000 full names annotated with genders. This dataset is available on our website for research purposes. In addition, this paper describes six machine learning algorithms (Support Vector Machine, Multinomial Naive Bayes, Bernoulli Naive Bayes, Decision Tree, Random Forrest and Logistic Regression) and a deep learning model (LSTM) with fastText word embedding for gender prediction on Vietnamese names. We create a dataset and investigate the impact of each name component on detecting gender. As a result, the best F1-score that we have achieved is up to 96% on LSTM model and we generate a web API based on our trained model.

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Section: ) Decision Tree (Dt)mentioning

confidence: 99%

Gender Prediction Based on Vietnamese Names with Machine Learning Techniques

Nguyen

et al. 2020

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

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“…Also, based on [13] the complexity of a tree will tends to affect the result of accuracy for a tree to do decision making. According to [14] DT much more convenient to do classification when it involved decision making, instead of able to compute both categorical and numerical data, it easily accessible and interpreted, involved less calculation, capable to illustrates relationship between dependent and independent variables and computationally low end. For document auto classification, DT is suitable to be applied into a simple framework that setting a set of rules and used for decision making to classify document based on its content into its category.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

Document Categorization Using Decision Tree: Preliminary Study

Noormanshah¹,

Nohuddin²,

Zainol³

2018

IJET

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This preliminaries study aims to propose a good classification technique that capable of doing document classification based on text mining technique and create an algorithm to automatically classify document according to its folder based on document’s content while able to do sentiment analyses to data sets and summarize it. The objective of this paper to identify an efficient text mining classification technique which can resulted with highest accuracy of classifying document into document folder, capable of extracting valuable information from context-based term that can be used as an output for algorithm to do automatic classification and evaluate the classification technique. Methodology of this study comprises in 5 modules which is 1) Document collection, 2) Pre-Processing Stage, 3) Term Frequency-Inversed Document Frequency, 4) Classification Technique and Algorithm, and lastly 5) Evaluation and Visualization of the classification result. The proposed framework will have utilized Term Frequency-Inversed Document Frequency (TF-IDF) and Decision Tree technique which TF-IDF used as purposes to rank all the terms based on most frequent to least frequent terms so, while decision tree function as decision making in terms of deciding which folder the document belongs to.

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Gender Prediction Based on Arabic Names with Machine Learning Techniques

Almelabi

Baashirah²

2022

Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic

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Different Classification Technique for Data mining in Insurance Industry using weka

Cited by 4 publications

References 4 publications

Gender Prediction Based on Vietnamese Names with Machine Learning Techniques

Gender Prediction Based on Vietnamese Names with Machine Learning Techniques

Document Categorization Using Decision Tree: Preliminary Study

Gender Prediction Based on Arabic Names with Machine Learning Techniques

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