Significance of machine learning algorithms in professional blogger's classification

Asim, Yousra; Raza, Basit

doi:10.1016/j.compeleceng.2017.08.001

Cited by 27 publications

(14 citation statements)

References 11 publications

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“…The main objective of this research is to model, develop, and implement an adaptive framework for the identification of influential bloggers by using blogger features and blog content features which can boost prediction results. It is an effort which extends our previous investigations [19][20][21] in this domain. This work can be differentiated with respect to the prior works by incorporating unstructured data handling along with labeled data in the suggested framework, provided the data is imbalanced.…”

Section: Introductionsupporting

confidence: 66%

See 1 more Smart Citation

Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Asim

Raza

et al. 2021

IEEE Access

Self Cite

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Section: Introductionsupporting

confidence: 66%

“…To overcome this preceding research gap, [20] was an initiative in this problem domain. By using a number of ML techniques, they have observed 85% accuracy gain for blogger classification by using RF classifier and Nearest Neighbor classifier along with standard way of cross validation.…”

Section: Related Workmentioning

confidence: 99%

Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Asim

Raza

et al. 2021

IEEE Access

Self Cite

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“…An algorithm's performance depends on a host of factors including the algorithm structure, parameter selection, the nature of the data, and the size of the dataset (Asim et al ). Different input combination scenarios should be considered along with statistical analysis (e.g., correlation and PCA) to obtain the best performance for a predictive model.…”

Section: Resultsmentioning

confidence: 99%

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

Khosravi¹,

Barzegar

Miraki

et al. 2019

Groundwater

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While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance‐based K‐nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree‐based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., normalCa2+,normalMg2+,normalNa+,normalK+,normalHCO3−,normalCO32−,normalSO42−,andnormalCl− concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.

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“…Ensemble learning refers to procedures employed to train multiple classifiers by combining their outputs and considering them as a "committee" of decision makers. Various approaches accomplish this learning concept, for example, bagging, boosting, AdaBoost (adaptive boosting), stacked generalisation, mixtures of experts, and voting based methods [2,5,11,14,40,42,58,73]. The presented approach is closely related to stacked generalisation [66,67,70].…”

Section: Related Work Vs Proposed Approachmentioning

confidence: 99%

Recognising innovative companies by using a diversified stacked generalisation method for website classification

Mirończuk

Protasiewicz

2019

Appl Intell

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In this paper, we propose a classification system which is able to decide whether a company is innovative or not, based only on its public website available on the internet. As innovativeness plays a crucial role in the development of myriad branches of the modern economy, an increasing number of entities are expending effort to be innovative. Thus, a new issue has appeared: how can we recognise them? Not only is grasping the idea of innovativeness challenging for humans, but also impossible for any known machine learning algorithm. Therefore, we propose a new indirect technique: a diversified stacked generalisation method, which is based on a combination of a multi-view approach and a genetic algorithm. The proposed approach achieves better performance than all other classification methods which include: (i) models trained on single datasets; or (ii) a simple voting method on these models. Furthermore, in this study, we check if unaligned feature space improves classification results. The proposed solution has been extensively evaluated on real data collected from companies' websites. The experimental results verify that the proposed method improves the classification quality of websites which might represent innovative companies.

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Significance of machine learning algorithms in professional blogger's classification

Cited by 27 publications

References 11 publications

Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Predicting Influential Blogger’s by a Novel, Hybrid and Optimized Case Based Reasoning Approach With Balanced Random Forest Using Imbalanced Data

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

Recognising innovative companies by using a diversified stacked generalisation method for website classification

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