To Ameliorate Classification Accuracy Using Ensemble Vote Approach and Base Classifiers

Ashraf, Mudasir; Zaman, Majid; Ahmed, Muheet

doi:10.1007/978-981-13-1498-8_29

Cited by 19 publications

(3 citation statements)

References 8 publications

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“…The result is that the KNN algorithm has the highest accuracy, precision, recall, f-measure, and Matthews correlation coefficient (MCC), respectively, 98.69%, 97.89%, 99.49%, 98.69%, and 97.39% [39]. A dataset of student performance is used to make predictions with classification techniques supported by the ensemble voting method [40]. The classification algorithm used is NB, KNN, conjunctive rules, and Hoeffding tree.…”

Section: The Comprehensive Theoretical Basismentioning

confidence: 99%

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

Kristiyanti,

Sanjaya,

Tjokro

et al. 2024

IJ-AI

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<span lang="EN-US">Global recession news dominates social media, particularly in Indonesia, with social news platforms on Twitter generating public responses and <br /> re-tweetings on the issue. Mining these opinions from Twitter using a sentiment analysis approach yields invaluable insights. The research stages included data collection, pre-processing, data labeling using the <br /> lexical-based method like valence aware dictionary and sentiment reasoner (VADER) and TextBlob, sampling techniques using synthetic minority oversampling technique (SMOTE) and random over sampling (ROS) before and after splitting data, and modeling using machine learning such as support vector machines (SVM), k-nearest neighbour (KNN), naive Bayes, and model evaluation. The problem is that almost 300,000 data collected from NodeXL are unbalanced. The findings show that models with balanced datasets show better model evaluation results. The sampling technique was carried out before and after splitting the data. The model evaluation results show that the Bernoulli-naive Bayes algorithm, with the VADER labeling technique, and the SMOTE sampling technique after splitting data, obtains the best accuracy of 84%, and using the ROS technique obtains an accuracy of 81%. On the other hand, with the SMOTE and ROS technique before splitting data on the SVM algorithm, it gets the best accuracy of 93% from before if only using SVM only reached 84%.</span>

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Section: The Comprehensive Theoretical Basismentioning

confidence: 99%

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

Kristiyanti,

Sanjaya,

Tjokro

et al. 2024

IJ-AI

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“…Since, a lot of data is present in every field like academic data [29][30][31][32][33], agricultural data [34], weather data [35][36][37][38], cloud data [39] and other type of data [40][41][42][43][44][45][46][47] etc. Here, in this study the dataset used in this operation was collected from 2012-2017 of Kashmir province.…”

Section: 21the Modelmentioning

confidence: 99%

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Fayaz¹,

Zaman²,

Butt³

2021

IJATEE

Self Cite

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Weather forecasting is considered as one of the approaches which are used to check the state of the atmosphere in the future at a specified location. A type of weather forecasting called as rainfall prediction has the highest influence in the farming & agricultural sector and other various sectors like natural disaster management etc. Accurate and timely rainfall prediction is one of the important factors in today's environment. Since the rainfall parameters keep on changing around the world in different places have different factors which are responsible for the rainfall. These parameters include the wind, vapour pressure, humidity at different intervals, temperature, season, evaporation etc. and are considered as the important meteorological parameters for predicting the rainfall in future time [1].*Author for correspondence Decision tree algorithms have been a choice of tool in the machine learning algorithm over the past decade. It was followed up by its modified version, including the more successful one i.e., random forest. However, there has been more precision and accuracy in modified versions of the various decision tree Re search Article

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“…With time it has spread into many business areas [1][2][3] and is originating in medical field too with the increase in the complication and evolution of data in biological sciences, AI is gradually being applied within the field. The AI technologies such as machine learning (ML) and deep learning (DL) [4][5][6][7] have played a foremost role in the detection and prediction of diseases [8,9] either by means of the disease symptom datasets [10,11] or medical image datasets, which have helped the doctors in a positive way.…”

Section: Introductionmentioning

confidence: 99%

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

Altaf

Butt²,

Zaman³

2022

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To predict and detect various diseases, machine learning techniques are increasingly being used in the field of medical science. This study puts forward a bagging meta-estimator and feed forward neural network based voting ensemble with mean decrease in impurity feature selection to classify the disease datasets. The work was carried out using the Jupyter notebook data analysis tool, and Python 3 is used as a programming language. In this study, two chronic disease datasets - Indian Liver Patient dataset and the PIMA Indians diabetes dataset are used for building and testing the proposed model. The datasets are split into training and testing data in the ratio of 70:30. The experimental results illustrate that our proposed voting ensemble has an improved performance compared to the individual base learners. We also compared the accuracy of the model before and after the application of feature reduction technique. The results revealed that the accuracy increased with the removal of unimportant features. By using the proposed ensemble model, the average MSE, bias and variance were calculated as 0.311, 0.217 and 0.094 respectively for ILPD dataset. Similarly for PIMA dataset, the average MSE, bias and variance were calculated as 0.233, 0.186 and 0.047 respectively. These statistical parameters record a low score for ensemble classifier as compared to the individual constituent classifiers.

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To Ameliorate Classification Accuracy Using Ensemble Vote Approach and Base Classifiers

Cited by 19 publications

References 8 publications

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

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