A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

scholarly journals News Classification Using Machine Learning

2021 ◽  
Vol 9 (5) ◽  
pp. 23-27
Author(s):  
SHWETA MAHAJAN
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Performance Improvement ◽  
Vital Role ◽  
Learning Approach ◽  
Entertainment Education ◽  
Meaningful Information ◽  
Textual Data ◽  
Machine Learning Approach

There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.

A Comparison of Rule-Based and Machine Learning Methods for Classification of Spikes in EEG

2017 ◽  
Author(s):  
Wolfgang Ganglberger ◽  
◽  
Gerhard Gritsch ◽  
Manfred M. Hartmann ◽  
Franz Fürbass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Rule Based ◽  
Machine Learning Methods

A Comparison of Rule-Based and Machine Learning Methods for Classification of Spikes in EEG

2017 ◽  
pp. 589-595 ◽  
Author(s):  
Wolfgang Ganglberger ◽  
◽  
Gerhard Gritsch ◽  
Manfred M. Hartmann ◽  
Franz Fürbass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Rule Based ◽  
Machine Learning Methods

scholarly journals Fine-grained classification of social science journal articles using textual data: A comparison of supervised machine learning approaches

2020 ◽  
pp. 1-26
Author(s):  
Joshua Eykens ◽  
Raf Guns ◽  
Tim C.E. Engels
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Social Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Fine Grained ◽  
Textual Data

We compare two supervised machine learning algorithms—Multinomial Naïve Bayes and Gradient Boosting—to classify social science articles using textual data. The high level of granularity of the classification scheme used and the possibility that multiple categories are assigned to a document make this task challenging. To collect the training data, we query three discipline specific thesauri to retrieve articles corresponding to specialties in the classification. The resulting dataset consists of 113,909 records and covers 245 specialties, aggregated into 31 subdisciplines from three disciplines. Experts were consulted to validate the thesauri-based classification. The resulting multi-label dataset is used to train the machine learning algorithms in different configurations. We deploy a multi-label classifier chaining model, allowing for an arbitrary number of categories to be assigned to each document. The best results are obtained with Gradient Boosting. The approach does not rely on citation data. It can be applied in settings where such information is not available. We conclude that fine-grained text-based classification of social sciences publications at a subdisciplinary level is a hard task, for humans and machines alike. A combination of human expertise and machine learning is suggested as a way forward to improve the classification of social sciences documents.

scholarly journals A Human Factors Engineering Education Perspective on Data Science, Machine Learning and Automation

2019 ◽  
Vol 63 (1) ◽  
pp. 488-492
Author(s):  
Daniel Hannon ◽  
Esa Rantanen ◽  
Ben Sawyer ◽  
Raymond Ptucha ◽  
Ashley Hughes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Engineering Education ◽  
Data Science ◽  
Human Factors Engineering ◽  
Automated Systems ◽  
Learning Techniques ◽  
Black Boxes ◽  
Model Training ◽  
Novel Applications

The explosion of data science (DS) in all areas of technology coupled with the rapid growth of machine learning (ML) techniques in the last decade create novel applications in automation. Many working with DS techniques rely on the concept of “black boxes” to explain how ML works, noting that algorithms find patterns in the data that humans might not. While the mathematics are still being developed, the implications for the application of ML, specifically to questions of automation, also are being studied, but still remain poorly understood. The decisions made by ML practitioners with respect to data selection, model training and testing, data visualization, and model applications remain relatively unconstrained and have the potential to yield unexpected results at the systems level. Unfortunately, human factors engineers concerned with automation often have limited training and awareness of DS and ML applications and are unable to provide the meaningful guidance that is needed to ensure the future safety of these newly emerging automated systems. Moreover, undergraduate and graduate programs in human factors engineering (HFE) have not kept pace with these developments and future HFEs may continue to find themselves unable to contribute meaningfully to the development of automated systems based on algorithms derived from ML. In this paper, human factors engineers and educators explore some of the challenges to our understanding of automation posed by specific ML techniques and contrast this with an outline of some of the historical work in HFE that has contributed to our understanding of safe and effective automation. Examples are provided from more conventional applications using both supervised and unsupervised learning techniques, that are explored with respect to implications for algorithm performance, use in system automation, and the potential for unintended results. Implications for human factors engineering education are discussed.

Classification of different skarn deposits based on the compositional variability of associated grandite garnets: a data science and Machine Learning approach

2021 ◽  
Author(s):  
Urmi Ghosh ◽  
Tuhin Chakraborty
Keyword(s):  
Machine Learning ◽  
Trace Element ◽  
Data Science ◽  
Training Dataset ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Skarn Deposits

<p>Rapid technological improvements made in in-situ analysis techniques, including LA-ICPMS, have transformed the field of analytical geochemistry. This has a far-reaching impact for different petrogenetic and ore-genetic studies where minute major and trace element compositional changes between different mineral zones within a single crystal can now be demarcated. Minerals such as garnet although robust are quite sensitive to the changing P-T and fluid conditions during their formation. These minerals have become powerful tools to characterize mineralization types. Previously, Meinert (1992) has used in-situ major element EPMA analysis results to classify different skarn deposit based on the end-member composition of hydrothermal garnets. Alternatively, Tian et al. (2019) used the garnet trace element composition for the similar purpose. However, these discrimination plots/ classification schemes show major overlap in different skarn deposits, such as Fe, Cu, Zn, and Au. The present study is an attempt to use machine learning approach on available garnet data to found a more potent classification scheme for skarn deposits, thus reaffirming garnet as a faithful indicator for hydrothermal ore deposits. We have meticulously collected major and trace element data of Ca-rich garnets, associated with different skarn deposits worldwide from 40 publications. This collected data is then used to train a model for fingerprinting the skarn deposits. Stratified random sampling method has been used on the dataset with 80% of the samples as test set and the rest 20 % as training dataset. We have used K-nearest neighbour (KNN), Support Vector Machine (SVM) and Random Forest algorithms on the data by using Python as a platform. These ML classification algorithm performs better than the earlier existing models available for classification of ore types based on garnet composition in skarn system. Factor importance is calculated that shows which elements play a pivotal role in classification of the ore type. Our results depict that multiple garnet forming elements taken together can reliably be used to discriminate between different ore formation settings.</p>

XCSR Learning from Compressed Data Acquired by Deep Neural Network

2017 ◽  
Vol 21 (5) ◽  
pp. 856-867 ◽  
Author(s):  
Kazuma Matsumoto ◽  
Takato Tatsumi ◽  
Hiroyuki Sato ◽  
Tim Kovacs ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning Classifier System ◽  
Rule Based ◽  
Classifier System ◽  
Learning Classifier ◽  
The Neural Network ◽  
Compressed Data

The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate of classification of the proposed system is higher than the conventional LCS (XCSR) and normal neural network; and 2) the covering mechanism of XCSR raises the correctness rate of proposed system.

scholarly journals Comparative Analysis of Expert and Machine-Learning Methods for Classification of Body Cavity Effusions in Companion Animals

2005 ◽  
Vol 17 (2) ◽  
pp. 158-164 ◽  
Author(s):  
Christine S. Hotz ◽  
Steven J. Templeton ◽  
Mary M. Christopher
Keyword(s):  
Machine Learning ◽  
Clinical Laboratory ◽  
Companion Animals ◽  
Body Cavity ◽  
Learning Methods ◽  
Rule Based ◽  
Machine Learning Methods ◽  
Patient Database ◽  
The University

A rule-based expert system using CLIPS programming language was created to classify body cavity effusions as transudates, modified transudates, exudates, chylous, and hemorrhagic effusions. The diagnostic accuracy of the rule-based system was compared with that produced by 2 machine-learning methods: Rosetta, a rough sets algorithm and RIPPER, a rule-induction method. Results of 508 body cavity fluid analyses (canine, feline, equine) obtained from the University of California–Davis Veterinary Medical Teaching Hospital computerized patient database were used to test CLIPS and to test and train RIPPER and Rosetta. The CLIPS system, using 17 rules, achieved an accuracy of 93.5% compared with pathologist consensus diagnoses. Rosetta accurately classified 91% of effusions by using 5,479 rules. RIPPER achieved the greatest accuracy (95.5%) using only 10 rules. When the original rules of the CLIPS application were replaced with those of RIPPER, the accuracy rates were identical. These results suggest that both rule-based expert systems and machine-learning methods hold promise for the preliminary classification of body fluids in the clinical laboratory.

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