Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Mirzamomen, Zahra; Kangavari, Mohammad Reza

doi:10.1007/s11063-016-9528-8

Cited by 15 publications

(7 citation statements)

References 33 publications

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“…Data ingestion is the crucial phase in maintaining large datasets and accessing them for knowledge discovery. It takes two forms such as batch processing and streaming ingestion (Mirzamomen & Kangavari, 2017). It could also be scaled up using cloud technologies with little efforts.…”

Section: Resultsmentioning

confidence: 99%

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Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

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Section: Resultsmentioning

confidence: 99%

“…An evolving fuzzy min-max decision tree learning algorithm is recommended in this direction for future researchers. It splits non-linearly to produce shallow trees that increase precision (Mirzamomen & Kangavari, 2017).…”

Section: Decision Treesmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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“…Zahra et. al., proposed the incremental decision tree based algorithm evolving fuzzy min max decision tree (EFMMDT) [12] in which every internal node has dynamic splitting logic. This logic is self sufficient to train itself based upon the data arrival.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

Critical evaluation of classifiers in data stream mining

Agrawal¹,

Adane²

2018

IJET

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Over past decade there has been a significant increase in the volume of online data. Extracting meaningful knowledge from this high volume data is considered as important aspect of research. It is very difficult to completely store full data, because of its perpetual nature. Therefore, analysis is needed while the “data is moving”. This moving data is known as data stream and analyzing it without storing it completely is termed as data stream mining. In recent years, many new techniques have been proposed to overcome the challenges of data stream mining. In this paper, we review the operation of popular streaming algorithms highlighting their strength and weaknesses. We also evaluate the classifiers used in these algorithms against two popular benchmark datasets namely (a) forest cover (forest) and (b) german credit available at UCI repository. Finally, we present our critical observation and draw conclusions on the basis of our analysis.

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“…With the rise of three-course system, udacity and coursera, the goal of students' systematic learning has certain feasibility. Although the online education platform can meet the needs of all kinds of people, the current online education evaluation method still needs further exploration [2]. It is worth noting that decision tree classi cation has become a very important evaluation classi cation method, and it is still e ective for the uncertain classi cation problem decision tree algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…is fuzzy decision tree has achieved certain research results in many industries such as medical, educational, and energy [2]. In view of the fact that traditional physical education has completely failed to meet the needs of "Internet plus education," a MOOCS model of college physical education is studied and a fuzzy decision tree algorithm is introduced to evaluate the MOOCS mode of physical education in colleges and universities, aiming at providing suggestions for the future development of MOOCS mode in college physical education.…”

Section: Introductionmentioning

confidence: 99%

Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm

Yang

2022

Mathematical Problems in Engineering

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With the continuous development of the MOOCS model in college physical education, the corresponding teaching evaluation has also been widely concerned by the community. The development of a traditional education mode in college physical education cannot meet the current teaching requirements. In order to solve the problem of narrow application and insufficient accuracy in traditional education, on the basis of the Kohonen fuzzy decision tree algorithm and the MOOCS fuzzy decision tree algorithm, a fuzzy evaluation model of P.E. teaching is proposed. The results show that the fuzzy ID3 algorithm can achieve high accuracy in the four databases, and the classification accuracy of the A-D database is 75.9%, 62.9%, 76.6%, and 95.1%, respectively. Except for database C, the classification accuracy of the minimum classification uncertainty algorithm is lower than the other two clear decision tree algorithms. Compared with the minimum classification uncertainty algorithm, the fuzzy ID3 algorithm has obvious advantages in classification rules. The classification rules of A-D database are 18, 12, 16, and 10, respectively. When the authenticity threshold is about 0.8, the fuzzy ID3 algorithm has the highest classification accuracy. The proposed MOOCS model for college physical education based on the fuzzy decision tree algorithm has strong practicability and high accuracy. This paper studies the MOOCS model of college physical education and introduces the fuzzy decision tree algorithm to evaluate the MOOCS model of college physical education. It solves the problem that traditional sports cannot satisfy the needs of “Internet plus education” at all. Compared with the traditional sports model, it has better applicability and higher accuracy.

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Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Cited by 15 publications

References 33 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

Critical evaluation of classifiers in data stream mining

Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm

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