Open AccessComparative Analysis of Machine Learning Algorithms with and without Feature Extraction
Author(s) -
Vatsal Gupta and Saurabh Gautam
Publication year - 2020
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst061243
Subject(s) - artificial intelligence , computer science , machine learning , feature extraction , random forest , classifier (uml) , support vector machine , convolutional neural network , pattern recognition (psychology) , dimensionality reduction , curse of dimensionality
Image recognition is one of the core disciplines in Computer Vision. It is one of the most widely researchedtopics of the last few decades. Many advances in image recognition in the past decade, has made it one of themost efficient and powerful disciplines of all, having its applications in every sector including Finance,Healthcare, Security services, Agriculture and many more. Feature extraction is an integral part of imagerecognition. It helps in training the model more efficiently and with a higher accuracy, by getting rid of anyunwanted or unnecessary features, thus reducing the dimensionality of the input image. This also helps inreducing the computational resources required by the algorithm to train, thus making it affordable for peoplewith low end setups. Here we compare the accuracies of different machine learning classification algorithms,and their training times, with and without using feature Extraction. For the purpose of extracting features, aconvolutional neural network was used. The model was trained and tested on the data of 12 classescontaining a total of 2,175 images. For comparisons, we chose the Logistic regression, K-Nearest NeighborsClassifier, Random forest Classifier, and Support Vector Machine Classifier.