Heracles: A framework for developing and evaluating text mining algorithms

Schouten, Kim; Frăsincar, Flavius; Dekker, Rommert; Riezebos, Mark

doi:10.1016/j.eswa.2019.03.005

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…To calculate the entropy value, the formula as in equation 1 is used, while the information gain value uses the equation. [15], [16] 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 Where Values(a) are all possible values in the case set a. Yv is a subclass of Y with class v corresponding to class a. Yes are all values that correspond to a.…”

Section: Random Forestmentioning

confidence: 99%

Comparison of Tree Implementation, Regression Logistics, and Random Forest to Detect Iris Types

Mukodimah¹,

Fauzi

2021

JurnalTAM

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Iris is a genus of 260-300 species of flowering plants with striking flower colors and has a dominant color in each region. The name iris is taken from the Greek word for rainbow, which is also the name for the Greek goddess of the rainbow, Iris. The number of types of iris plants with almost the same physical characteristics, especially in the pistil and crown, causes the misdetection of iris plant types. Iris plants are deliberately used because data is already available digitally on the internet and software such as orange and is widely used as a material for classifying objects. This research was conducted to classify iris plant types using three algorithms, namely Tree algorithm, Regression Logistics, and Random Forest. Classification algorithms are a learning method for predicting the value of a group of attributes in describing and distinguishing a class of data or concepts that aim to predict a class of objects whose class labels are unknown. The results showed the largest AUC (Area Under Curve) value obtained by the Random Forest method. AUC accuracy is said to be perfect when the AUC value reaches 1,000 and the accuracy is poor if the AUC value is below 0.500. As for the precision value of the three models used Random Forest has the highest precision value. From the data tests that have been done training and testing can be seen that the level of accuracy of testing of the three models where the Random Forest model is superior as a method for classification of irises.

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Section: Random Forestmentioning

confidence: 99%

Comparison of Tree Implementation, Regression Logistics, and Random Forest to Detect Iris Types

Mukodimah¹,

Fauzi

2021

JurnalTAM

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“…Authors Schouten et al [6], proposed Heracles, the model which is implemented and evaluate the text mining techniques, and taken considerations of a variety of mining software solutions in industry4.0. As compared to many text mining approaches, Heracles work on both implementations and evaluation timeline.…”

Section: Study On Text-mining Techniquesmentioning

confidence: 99%

Design of Novel ETL Model to Analyse Corona Virus Data

Dewangan¹,

Ghosh²,

Shrivas³

2020

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The corona disease was first recognized in 2019 in Wuhan, which is the capital of China's Hubei-province, and from then it continued spreading and as a result declared as a pandemic by all nations. The COVID-19 virus has different effects on people in various ways. It is a kind of respiratory disease. The confirmed cases are increasing day to day in India, which leads to complete lockdown throughout the nation. OBJECTIVE: The objective of this research is to design a novel Extract-Trandform and Load NETL model to analyse covid-19 data in india. METHODS: The extraction of useful information from a large database is a well-connected research field of text mining. This paper is proposed a novel extract-transform-load ETL model to process the COVID-19 data of India to get the exact recovery data from the multiple data sources from different states of India. In this, a knowledgebased model that generate knowledge based on three different module split, validation, and join is discussed. RESULTS: The outcomes of the proposed NETL process are, output file which has the description of total positive cases, active cases, recovery cases, and death rate, based on different regions. The analysis of NETL is done based on accuracy, failure count, and execution time. The proposed NETL process is more accurate and taking less compilation time with minimum failure count as compared with existing models. CONCLUSION: To analyze the coronavirus data in India, a novel ETL (NETL) model is proposed. In this model, a total of 9 CSV files is processed as input files to get different results in different categories. This model is having three modules namely splitting, verification, and join. The dataset is split into based on its coupling attributes and then joined with a single value to produce the updated results as per the current dataset. The last stage of this process is to join the data which is generated through splitting. The proposed NETL model is more accurate as compared with existing ETM models.

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Reconstructing Scanned Documents for Full-Text Indexing to Empower Digital Library Services

Nitu

Dascălu

et al. 2020

Emerging Technologies for Education

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Heracles: A framework for developing and evaluating text mining algorithms

Cited by 9 publications

References 32 publications

Comparison of Tree Implementation, Regression Logistics, and Random Forest to Detect Iris Types

Comparison of Tree Implementation, Regression Logistics, and Random Forest to Detect Iris Types

Design of Novel ETL Model to Analyse Corona Virus Data

Reconstructing Scanned Documents for Full-Text Indexing to Empower Digital Library Services

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