PwAdaBoost: Possible world based AdaBoost algorithm for classifying uncertain data

Liu, Han; Zhang, Xianchao; Zhang, Xiaolin

doi:10.1016/j.knosys.2019.104930

Cited by 13 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the experimental results, amazon product review data from Kaggle [22] is tested and as well as tested against the realtime data obtained from the amazon product URL. Experimental results are compared with traditional models such as Adaboost with Random tree [16], KNN [18], Stacking Random tree [19], Bagging Random tree [20], Naïve Bayes Multinomial Text [21], RF [17] for performance analysis. The main problems identified in these models are 1.…”

Section: Resultsmentioning

confidence: 99%

A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

Syamala¹,

Nalini²

2020

IJIES

View full text Add to dashboard Cite

E-Commerce product features and reviews are considered to be the essential factors in real-time e-commerce sites for product recommendation systems. Due to inaccuracy decision patterns, in most cases e-commerce user fails to predict the products based on the user ratings and review comments. Traditional sentiment classification models are independent of data filtering, transformation and sentiment score computing techniques which require high computing memory, time and mostly leading to false-positive rate. To overcome these issues, a novel sentiment score-based product recommendation model is proposed on the real-time product data. In this model, a new product ranking score, filtering, and hybrid decision tree classifiers are implemented. Initially, real-time amazon product review data is captured using Document Object Model (DOM) parser. The features from the review comments are extracted using lexicon Feature Dictionary (FD) and AFINN, Normalized Product Review Score (NPRS) are generated to compute the class label for product review sentiment prediction. Ranked Principal Component Analysis (RPCA) is used as a feature selection measure to overcome the problem of data sparsity. Random Tree, Hoeffding Tree, Adaboost + Random Tree, the three variants of decision tree classifiers are used for product sentiment classification. The proposed filter-based improved decision tree sentiment classification model for real-time amazon product review data recommends the product based on the user query by prediction using a new novel normalized product review sentiment score and ranked feature selection measure. The proposed product recommendation, the decision-making system maximizes sentiment classification accuracy. Experimental results are compared against the traditional decisionmaking classification models in terms of correctly classified instances, error rate, and PRC, F-measure, kappa statistics. The proposed model experimental results show high efficiency.

show abstract

Section: Resultsmentioning

confidence: 99%

A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

Syamala¹,

Nalini²

2020

IJIES

View full text Add to dashboard Cite

show abstract

“…In each iteration of training, Adaboost focuses more on misclassified samples and generates a relatively good model at the end. The algorithm not only has a simple structure but also has high accuracy [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Adaboost does not need to select attributes for training samples.…”

Section: Preliminariesmentioning

confidence: 99%

“…As a data-driven classification method, Adaboost has been widely used in many fields, including classification [ 22 , 23 , 24 , 25 , 26 , 27 ], fault diagnosis [ 28 , 29 ], pattern recognition [ 30 , 31 , 32 ], prediction [ 33 , 34 , 35 , 36 ], energy [ 37 ], aviation [ 38 ], and medical [ 39 ] fields. Adaboost does not make any assumptions about the probability distribution of samples.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification

Wang

2021

Entropy

View full text Add to dashboard Cite

In the framework of evidence theory, one of the open and crucial issues is how to determine the basic probability assignment (BPA), which is directly related to whether the decision result is correct. This paper proposes a novel method for obtaining BPA based on Adaboost. The method uses training data to generate multiple strong classifiers for each attribute model, which is used to determine the BPA of the singleton proposition since the weights of classification provide necessary information for fundamental hypotheses. The BPA of the composite proposition is quantified by calculating the area ratio of the singleton proposition’s intersection region. The recursive formula of the area ratio of the intersection region is proposed, which is very useful for computer calculation. Finally, BPAs are combined by Dempster’s rule of combination. Using the proposed method to classify the Iris dataset, the experiment concludes that the total recognition rate is 96.53% and the classification accuracy is 90% when the training percentage is 10%. For the other datasets, the experiment results also show that the proposed method is reasonable and effective, and the proposed method performs well in the case of insufficient samples.

show abstract

Eine praktische Methode für die Vorhersage von Sohlwassereinbrüchen unter Verwendung eines Hybridmodells aus künstlicher Intelligenz und GIS

Liu

Han

2023

Mine Water Environ

View full text Add to dashboard Cite

PwAdaBoost: Possible world based AdaBoost algorithm for classifying uncertain data

Cited by 13 publications

References 44 publications

A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

A Filter Based Improved Decision Tree Sentiment Classification Model for RealTime Amazon Product Review Data

A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification

Eine praktische Methode für die Vorhersage von Sohlwassereinbrüchen unter Verwendung eines Hybridmodells aus künstlicher Intelligenz und GIS

Contact Info

Product

Resources

About