FUZZY kNNMODEL APPLIED TO PREDICTIVE TOXICOLOGY DATA MINING

Guo, Gongde; Neagu, Daniel

doi:10.1142/s1469026805001635

Cited by 4 publications

(2 citation statements)

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“…Traditional manual data analysis has become inefficient, and computer-based analysis is indispensable. Statistical methods (1), expert systems (2), fuzzy neural networks (3) and other machine learning algorithms (4,5), are extensively studied and applied to predictive toxicology for model development and decision making. However, due to the complexity of modelling existing toxicity data sets caused by numerous irrelevant descriptors, skewed distribution, missing values and noisy data, no dominant machine learning algorithm can be proposed to accurately model all of the toxicity data sets available.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Machine Learning Algorithms Applied to Predictive Toxicology Data Mining

et al. 2007

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This paper reports results of a comparative study of widely used machine learning algorithms applied to predictive toxicology data mining. The machine learning algorithms involved were chosen in terms of their representability and diversity, and were extensively evaluated with seven toxicity data sets which were taken from real-world applications. Some results based on visual analysis of the correlations of different descriptors to the class values of chemical compounds, and on the relationships of the range of chosen descriptors to the performance of machine learning algorithms, are emphasised from our experiments. Some interesting findings relating to the data and the quality of the models are presented — for example, that no specific algorithm appears best for all seven toxicity data sets, and that up to five descriptors are sufficient for creating classification models for each toxicity data set with good accuracy. We suggest that, for a specific data set, model accuracy is affected by the feature selection method and model development technique. Models built with too many or too few descriptors are undesirable, and finding the optimal feature subset appears at least as important as selecting appropriate algorithms with which to build a final model.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Machine Learning Algorithms Applied to Predictive Toxicology Data Mining

et al. 2007

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“…Furthermore, Zhu et al [25] proposed data mining techniques to discover the correlations between sentences, and their utilization as feature values for classification using Support Vector Machines (SVM). In the field of classifiers, Guo et al [26] compared SVM, K-Nearest Neighbors algorithm (KNN) and Naive Bayes classifier (NB) for classification purposes.…”

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Semantic-Based Public Opinion Analysis System

Wang,

Su,

Zeng

et al. 2024

Electronics

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In the research into semantic sentiment analysis, researchers commonly use some factor rules, such as the utilization of emotional keywords and the manual definition of emotional rules, to increase accuracy. However, this approach often requires extensive data and time-consuming training, and there is a need to make the system simpler and more efficient. Recognizing these challenges, our paper introduces a new semantic sentiment analysis system designed to be both higher in quality and more efficient. The structure of our proposed system is organized into several key phases. Initially, we focus on data training, which involves studying emotions and emotional psychology. Utilizing linguistic resources such as HowNet and the Chinese Knowledge and Information Processing (CKIP) techniques, we develop emotional rules that facilitate the generation of sparse representation characteristics. This process also includes constructing a sparse representation dictionary. We can map these back to the original vector space by resolving the sparse coefficients, representing two distinct categories. The system then calculates the error compared to the original vector, and the category with the minimum error is determined. The second phase involves inputting topics and collecting relevant comments from internet forums to gather public opinion on trending topics. The final phase is data classification, where we assess the accuracy of classified issues based on our data training results. Additionally, our experimental results will demonstrate the system’s ability to identify hot topics, thus validating our semantic classification models. This comprehensive approach ensures a more streamlined and effective system for semantic sentiment analysis.

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A fuzzy vector valued KNN-algorithm for automatic outlier detection

Östermark

2009

Applied Soft Computing

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FUZZY kNNMODEL APPLIED TO PREDICTIVE TOXICOLOGY DATA MINING

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Cited by 4 publications

References 10 publications

A Comparative Study of Machine Learning Algorithms Applied to Predictive Toxicology Data Mining

A Comparative Study of Machine Learning Algorithms Applied to Predictive Toxicology Data Mining

Semantic-Based Public Opinion Analysis System

A fuzzy vector valued KNN-algorithm for automatic outlier detection

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