A Scalable Feature Selection and Model Updating Approach for Big Data Machine Learning

2016 ◽  
Author(s):  
Baijian Yang ◽  
Tonglin Zhang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Model Updating

scholarly journals Streaming feature selection algorithms for big data: A survey

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Noura AlNuaimi ◽  
Mohammad Mehedy Masud ◽  
Mohamed Adel Serhani ◽  
Nazar Zaki
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Real Time ◽  
Relevant Literature ◽  
Heterogeneous Data ◽  
Proper Solution ◽  
Exact Figure ◽  
Selection Algorithms ◽  
Over Time

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

scholarly journals Machine learning-driven automatic storage space recommendation for object-based cloud storage system

2021 ◽  
Author(s):  
Anindita Sarkar Mondal ◽  
Anirban Mukhopadhyay ◽  
Samiran Chattopadhyay
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Cloud Storage ◽  
Recommendation System ◽  
Storage System ◽  
Sparse Learning ◽  
Storage Space ◽  
Object Based ◽  
Selection Algorithms

AbstractAn object-based cloud storage system is a storage platform where big data is managed through the internet and data is considered as an object. A smart storage system should be able to handle the big data variety property by recommending the storage space for each data type automatically. Machine learning can help make a storage system automatic. This article proposes a classification engine framework for this purpose by utilizing a machine learning strategy. A feature selection approach wrapped with a classifier is proposed to automatically predict the proper storage space for the incoming big data. It helps build an automatic storage space recommendation system for an object-based cloud storage platform. To find out a suitable combination of feature selection algorithms and classifiers for the proposed classification engine, a comparative study of different supervised feature selection algorithms (i.e., Fisher score, F-score, Lll21) from three categories (similarity, statistical, sparse learning) associated with various classifiers (i.e., SVM, K-NN, Neural Network) is performed. We illustrate our study using RSoS system as it provides a cloud storage platform for the healthcare data as experimental big data by considering its variety property. The experiments confirm that Lll21 feature selection combined with K-NN classifier provides better performance than the others.

scholarly journals A New Big Data Feature Selection Approach for Text Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Houda Amazal ◽  
Mohamed Kissi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Mutual Information ◽  
Text Classification ◽  
Programming Model ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Term Frequency ◽  
Text Feature

Feature selection (FS) is a fundamental task for text classification problems. Text feature selection aims to represent documents using the most relevant features. This process can reduce the size of datasets and improve the performance of the machine learning algorithms. Many researchers have focused on elaborating efficient FS techniques. However, most of the proposed approaches are evaluated for small datasets and validated using single machines. As textual data dimensionality becomes higher, traditional FS methods must be improved and parallelized to handle textual big data. This paper proposes a distributed approach for feature selection based on mutual information (MI) method, which is widely applied in pattern recognition and machine learning. A drawback of MI is that it ignores the frequency of the terms during the selection of features. The proposal introduces a distributed FS method, namely, Maximum Term Frequency-Mutual Information (MTF-MI), based on term frequency and mutual information techniques to improve the quality of the selected features. The proposed approach is implemented on Hadoop using the MapReduce programming model. The effectiveness of MTF-MI is demonstrated through several text classification experiments using the multinomial Naïve Bayes classifier on three datasets. Through a series of tests, the results reveal that the proposed MTF-MI method improves the classification results compared with four state-of-the-art methods in terms of macro-F1 and micro-F1 measures.

Machine-Learning-Based Image Feature Selection

2018 ◽  
pp. 65-73
Author(s):  
Vivek K. Verma ◽  
Tarun Jain
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Big Data ◽  
Image Retrieval ◽  
Search Algorithms ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Large Databases ◽  
Retrieval Problem

This is the age of big data where aggregating information is simple and keeping it economical. Tragically, as the measure of machine intelligible data builds, the capacity to comprehend and make utilization of it doesn't keep pace with its development. In content-based image retrieval (CBIR) applications, every database needs its comparing parameter setting for feature extraction. CBIR is the application of computer vision techniques to the image retrieval problem that is the problem of searching for digital images in large databases. In any case, the vast majority of the CBIR frameworks perform ordering by an arrangement of settled and pre-particular parameters. All the major machine-learning-based search algorithms have discussed in this chapter for better understanding related with the image retrieval accuracy. The efficiency of FS using machine learning compared with some other search algorithms and observed for the improvement of the CBIR system.

Machine learning based feature selection and knowledge reasoning for CBR system under big data

2021 ◽  
Vol 112 ◽  
pp. 107805
Author(s):  
Yuan Guo ◽  
Bing Zhang ◽  
Y. Sun ◽  
K. Jiang ◽  
K. Wu
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Knowledge Reasoning

scholarly journals Feature Selection for Machine Learning in Big Data

2019 ◽  
Vol 8 (6S4) ◽  
pp. 332-335
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Big Data ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Information Age ◽  
Machine Learning Algorithms ◽  
Fine Grained ◽  
Selection For ◽  
Selection Mechanisms

We are in the information age there by collecting very huge volume of data from diverse sources in structured, unstructured and semi structured form ranging to petabytes to exabytes of data. Data is an asset as valuable knowledge and information is hidden in such massive volumes of data. Data analytics is required to have a deeper insights and identify fine grained patterns so as to make accurate predictions enabling the improvement of decision making. Extracting knowledge from data is done by data analytics, Machine learning forms the core of it. The increase in the dimensionality of data both in terms of number of tuples and also in terms of number of features poses several challenges to the machine learning algorithms . Preprocessing of data is done as a prior step to machine learning, so feature selection is done as a preprocessing step to have the dimensionality reduction of the data and thereby removing the irrelevant features and improving the efficiency and accuracy of a machine learning algorithm. In this paper we are studying various feature selection mechanisms and analyze them whether they can be adopted to sentiment analysis of big data.

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