Proceedings ArticleDOI
Predictive Analysis of Heterogeneous Data – Techniques & Tools
Jayshree Ghorpade,Balwant A. Sonkamble +1 more
- pp 40-44
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TLDR
Different Machine Learning techniques and tools are studied to perform predictive analysis of the continuous as well as categorical data.Abstract:
Data intensive processing and analysis has lead new research avenues to draw the attention of researchers and decision makers in the field of data and information sciences The exponential rise in variety of data in today’s computerized era is laying new challenges for the society Nevertheless, there are huge potentials and useful information present in the data This undiscovered information is one of the most valuable assets for the data intensive scientific real-time applications It is a vital factor for the efficient outcome and evolving advances in various technical aspects Yet, a large number of sectors with varied data sources face difficulties with the variety of data Processing this heterogeneous data is necessary to extract useful information, making it a decisive factor for the better survival of future with automation Different Machine Learning techniques and tools are studied to perform predictive analysis of the continuous as well as categorical dataread more
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Book ChapterDOI
Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm
TL;DR: In this paper, an ensemble bootstrap genetic algorithm (EnBGA) is proposed to generate the effective feature subset for the multi-source heterogeneous data, where various univariate and multivariate base selectors are combined together to ensure the robustness and stability of the algorithm.
Proceedings ArticleDOI
Bi-Directional Chains of Neural Nets for Multi-Target Regression
TL;DR: In this paper, the significance of directionality problem has been discussed and is addressed by proposing an ensemble based methodology, which can be used in both classification and regression using chain models, which although mostly competent, possess the issue of a uni-directional dependency.
References
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TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
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Amir H. Gandomi,Murtaza Haider +1 more
TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.
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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
TL;DR: This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies currently adopt to deal with the Big Data problems.
Journal ArticleDOI
Big Data: A Survey
Min Chen,Shiwen Mao,Yunhao Liu +2 more
TL;DR: The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.
Journal ArticleDOI
Feature selection: evaluation, application, and small sample performance
Anil K. Jain,D. Zongker +1 more
TL;DR: This work studies the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models and shows that pooling features derived from different texture Models, followed by a feature selection results in a substantial improvement in the classification accuracy.