A Performance Comparisons of Machine Learning Classification Techniques for Job Titles Using Job Descriptions

A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.

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Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD)

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) ◽

10.1109/bibe.2017.00-39 ◽

2017 ◽

Cited By ~ 10

Author(s):

W.H.S.D Gunarathne ◽

K.D.M Perera ◽

K.A.D.C.P Kahandawaarachchi

Keyword(s):

Machine Learning ◽

Chronic Kidney Disease ◽

Performance Evaluation ◽

Kidney Disease ◽

Data Analytics ◽

Disease Classification ◽

Classification Techniques ◽

Machine Learning Classification

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EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-573-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 573-578 ◽

Cited By ~ 3

Author(s):

◽

S. S. Ray

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Classification Accuracy ◽

Ground Truth ◽

Kappa Coefficient ◽

Ground Truth Data ◽

Classification Techniques ◽

Machine Learning Classification ◽

Crop Classification ◽

Sentinel 2

<p><strong>Abstract.</strong> Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CART) and Support Vector Machine (SVM) for crop classification. High Resolution optical data, Sentinel-2, MSI (10&thinsp;m) was used for crop classification in the Indian Agricultural Research Institute (IARI) farm for the Rabi season 2016 for major crops. Around 100 crop fields (~400 Hectare) in IARI were analysed. Smart phone-based ground truth data were collected. The best cloud free image of Sentinel 2 MSI data (5 Feb 2016) was used for classification using automatic filtering by percentage cloud cover property using the GEE. Polygons as feature space was used as training data sets based on the ground truth data for crop classification using machine learning techniques. Post classification, accuracy assessment analysis was done through the generation of the confusion matrix (producer and user accuracy), kappa coefficient and F value. In this study it was found that using GEE through cloud platform, satellite data accessing, filtering and pre-processing of satellite data could be done very efficiently. In terms of overall classification accuracy and kappa coefficient, Random Forest (93.3%, 0.9178) and CART (73.4%, 0.6755) classifiers performed better than SVM (74.3%, 0.6867) classifier. For validation, Field Operation Service Unit (FOSU) division of IARI, data was used and encouraging results were obtained.</p>

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