Unsupervised classification of events: A semantic rule based on color moments of background and foreground method

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.

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Unsupervised classification of street architectures based on InfoGAN

10th International Conference on Pattern Recognition Systems (ICPRS-2019) ◽

10.1049/cp.2019.0241 ◽

2019 ◽

Author(s):

Ning Wang ◽

Xianhan Zeng ◽

Renjie Xie ◽

Zefei Gao ◽

Yi Zheng ◽

...

Keyword(s):

Unsupervised Classification

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Contextual unsupervised classification of remotely sensed imagery with mixels

10.1117/12.689566 ◽

2006 ◽

Cited By ~ 1

Author(s):

Shuji Kawaguchi ◽

Ryuei Nishii

Keyword(s):

Unsupervised Classification ◽

Remotely Sensed ◽

Remotely Sensed Imagery

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

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641034 ◽

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.

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