An Experimental Study of Diversity of Diabetes Disease Features by Bagging and Boosting Ensemble Method with Rule Based Machine Learning Classifier Algorithms

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Dhyan Chandra Yadav ◽  
Saurabh Pal
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Ensemble Method ◽  
Rule Based ◽  
Learning Classifier ◽  
Classifier Algorithms

scholarly journals Die ontwikkeling van ’n woordafbreker en kompositumanaliseerder vir Afrikaans

Literator ◽  
2008 ◽  
Vol 29 (1) ◽  
pp. 21-42 ◽  
Author(s):  
S. Pilon ◽  
M.J. Puttkammer ◽  
G.B. Van Huyssteen
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Practical Implementation ◽  
Manual Annotation ◽  
Machine Learning Technique ◽  
Rule Based ◽  
The Core ◽  
Learning Classifier ◽  
Learning Technique ◽  
Rule Based Approach

The development of a hyphenator and compound analyser for Afrikaans The development of two core-technologies for Afrikaans, viz. a hyphenator and a compound analyser is described in this article. As no annotated Afrikaans data existed prior to this project to serve as training data for a machine learning classifier, the core-technologies in question are first developed using a rule-based approach. The rule-based hyphenator and compound analyser are evaluated and the hyphenator obtains an fscore of 90,84%, while the compound analyser only reaches an f-score of 78,20%. Since these results are somewhat disappointing and/or insufficient for practical implementation, it was decided that a machine learning technique (memory-based learning) will be used instead. Training data for each of the two core-technologies is then developed using “TurboAnnotate”, an interface designed to improve the accuracy and speed of manual annotation. The hyphenator developed using machine learning has been trained with 39 943 words and reaches an fscore of 98,11% while the f-score of the compound analyser is 90,57% after being trained with 77 589 annotated words. It is concluded that machine learning (specifically memory-based learning) seems an appropriate approach for developing coretechnologies for Afrikaans.

scholarly journals Forest Fire Prediction using Machine Learning Models based on DC, Wind and RH

2020 ◽  
Vol 8 (6S) ◽  
pp. 142-143
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Forest Fire ◽  
Random Forest Algorithm ◽  
Learning Models ◽  
Learning Classifier ◽  
Machine Learning Models ◽  
Classifier Algorithms

The paper points out forest fire prediction using machine learning models on the basis of viz. DC, Wind, RH out of the several machine learning classifier algorithms, It is relevant that random forest algorithm generates optimum accuracy(99.61%).

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 Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

2009 ◽  
Vol 2009 ◽  
pp. 1-25 ◽  
Author(s):  
Ryan J. Urbanowicz ◽  
Jason H. Moore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Discovery ◽  
Evolutionary Biology ◽  
Historical Review ◽  
Machine Learning Algorithms ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Rule Based ◽  
Learning Classifier

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.

scholarly journals Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

2021 ◽  
Vol 13 (2) ◽  
pp. 971
Author(s):  
Papiya Debnath ◽  
Pankaj Chittora ◽  
Tulika Chakrabarti ◽  
Prasun Chakrabarti ◽  
Zbigniew Leonowicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vital Role ◽  
Earthquake Forecasting ◽  
Supervised Machine Learning ◽  
Learning Technology ◽  
Learning Classifier ◽  
Moderate Earthquake ◽  
Supervised Machine Learning Classifiers ◽  
Simple Logistic ◽  
Classifier Algorithms

Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case.

scholarly journals Discovering Fine-grained Sentiment in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8963 ◽  
Author(s):  
Wenbo Wang ◽  
Lu Chen ◽  
Ming Tan ◽  
Shaojun Wang ◽  
Amit P. Sheth
Keyword(s):  
Machine Learning ◽  
Hybrid System ◽  
Rule Based ◽  
Fine Grained ◽  
Knowledge Based ◽  
Learning Classifier ◽  
The Mean ◽  
Training Examples ◽  
Rule Based Classifier ◽  
Better Than

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.

scholarly journals Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data

2021 ◽  
Vol 13 (2) ◽  
pp. 38-48
Author(s):  
Golam Mostafa ◽  
◽  
Ikhtiar Ahmed ◽  
Masum Shah Junayed
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Personal Development ◽  
Data Science ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Learning Classifier ◽  
Twitter Data ◽  
Classifier Algorithms

In recent years, with the advancement of the internet, social media is a promising platform to explore what going on around the world, sharing opinions and personal development. Now, Sentiment analysis, also known as text mining is widely used in the data science sector. It is an analysis of textual data that describes subjective information available in the source and allows an rganization to identify the thoughts and feelings of their brand or goods or services while monitoring conversations and reviews online. Sentiment analysis of Twitter data is a very popular research work nowadays. Twitter is that kind of social media where many users express their opinion and feelings through small tweets and different machine learning classifier algorithms can be used to analyze those tweets. In this paper, some selected machine learning classifier algorithms were applied on crawled Twitter data after applying different types of preprocessors and encoding techniques, which ended up with satisfying accuracy. Later a comparison between the achieved accuracies was showed. Experimental evaluations show that the Neural Network Classifier’algorithm provides a remarkable accuracy of 81.33% compared with other classifiers.

Privacy Protection in Enterprise Social Networks Using a Hybrid De-Identification System

2021 ◽  
Vol 15 (1) ◽  
pp. 138-152
Author(s):  
Mohamed Abdou Souidi ◽  
Noria Taghezout
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Identification System ◽  
Sensitive Information ◽  
Rule Based ◽  
Learning Classifier ◽  
Enterprise Social Networks ◽  
Rule Based Classifier ◽  
Encryption Method ◽  
Very High

Enterprise social networks (ESN) have been widely used within organizations as a communication infrastructure that allows employees to collaborate with each other and share files and documents. The shared documents may contain a large amount of sensitive information that affect the privacy of persons such as phone numbers, which must be protected against any kind of disclosure or unauthorized access. In this study, authors propose a hybrid de-identification system that extract sensitive information from textual documents shared in ESNs. The system is based on both machine learning and rule-based classifiers. Gradient boosted trees (GBTs) algorithm is used as machine learning classifier. Experiments ran on a modified CoNLL 2003 dataset show that GBTs algorithm achieve a very high F1-score (95%). Additionally, the rule-based classifier is consisted of regular expression and gazetteers in order to complement the machine learning classifier. Thereafter, the sensitive information extracted by the two classifiers are merged and encrypted using Format Preserving Encryption method.

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.

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