A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset

Chaudhary, Archana; Kolhe, Savita; Kamal, Raj

doi:10.1016/j.compag.2016.03.026

Cited by 56 publications

(16 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, Naïve Bayesian is used as a classifier. This technique is useful because it can calculate the [55,56]. 12.51% (635 patients), 7.49% (380 patients) and 8.75% (444 patients) of the patients under study, were placed in the levels of BI-RADS 0 to BI-RADS V I respectively.…”

Section: Naïve Bayesian (Nb)mentioning

confidence: 99%

Automated classification of BI-RADS in textual mammography reports

Boroumandzadeh¹,

Parvinnia²

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

The main purpose of this paper is to process key information in medical text records and also classify patients, per different levels of Breast Imaging-Reporting and Data system (BI-RADS). The BI-RADS is a scheme for the standardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reports supported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction from textual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosing keywords from medical text reports. Word2vec and Term Frequency Inverse Document Frequency (TFIDF) techniques are used for extracting features, finally, they're combined with the Hospital Information System (HIS) reports and called With-HIS. The different classifiers like multi-class Support Vector Machine (SVM), Naïve Bayesian (NB), Extreme Gradient Boosting (XGBoost), and Multi-Level Fuzzy Min-Max Neural Network (MLF) are used so as to compare the accuracy of With-HIS and Without HIS (called Without-HIS). The results are confirmed that using HIS beside the proposed approach (Word2vec +TFIDF) encompasses a significant effect on the accuracy of medical text classification. Accuracy within the proposed method with MLF classifier is 0.89% but without-HIS is 0.85%.

show abstract

Section: Naïve Bayesian (Nb)mentioning

confidence: 99%

Automated classification of BI-RADS in textual mammography reports

Boroumandzadeh¹,

Parvinnia²

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…For example, stacked generalisation, classifier ensembles, hybrid methods and mixture of experts are some of the common ones. Previous works have shown that different combination of classifiers are employed to improve the performance of constituent classifiers [17][18][19]. However, in this work, these different terminologies about the classifier combination and the significant of information fusion are unified by adopting the following point of view, which is to search for the best set of classifiers by fusing the outputs of individual classifiers without considering the nature of feature set and pattern representation of input data.…”

Section: Related Workmentioning

confidence: 99%

Analysis of produce recognition system with taxonomist's knowledge using computer vision and different classifiers

Chaw

Mokji

2017

IET Image Processing

View full text Add to dashboard Cite

“…There is often a problem with multi-class dataset data due to improper classification of results due to the large collection and distribution of class results. From the increasing number of multiclass data sets Therefore, new machine learning algorithms [11][12][13] need to be developed to improve the efficiency of the results class prediction and find that there are multiple learning models that can be used to solve the same problem [14][15][16]. Ensemble learning is a machine learning process to improve the efficiency of predictions [17][18][19]., [20] using a strategy to combine multiple learning predictions and helps to reduce the problem of inappropriate model selection by combining all models.…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble learning is a machine learning process to improve the efficiency of predictions [17][18][19]., [20] using a strategy to combine multiple learning predictions and helps to reduce the problem of inappropriate model selection by combining all models. This method is popular and widely used to improve performance than individual models [13,21,22]. According to the effectiveness of the ensemble learning method, it is necessary to create Creating a new ensemble model to improve the model's accuracy and stability [16,20,23].…”

Section: Introductionmentioning

confidence: 99%

Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

Rojarath

Songpan

2021

Appl Intell

View full text Add to dashboard Cite

Ensemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive learning was implemented to evaluate the prediction accuracy for each class, which was used to construct a cost-sensitivity matrix of the true positive (TP) rate. This TP rate can be used as a weight value and combined with a probability value to drive ensemble learning for a specified class. We proposed an ensemble model, which was a type of heterogenous model, namely, a combination of various individual classification models (support vector machine, Bayes, K-nearest neighbour, naïve Bayes, decision tree, and multi-layer perceptron) in experiments on 3-, 4-, 5- and 6-classifier models. The efficiencies of the propose models were compared to those of the individual classifier model and homogenous models (Adaboost, bagging, stacking, voting, random forest, and random subspaces) with various multi-class data sets. The experimental results demonstrate that the cost-sensitive probability for the weighted voting ensemble model that was derived from 3 models provided the most accurate results for the dataset in multi-class prediction. The objective of this study was to increase the efficiency of predicting classification results in multi-class classification tasks and to improve the classification results.

show abstract

A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset

Cited by 56 publications

References 35 publications

Automated classification of BI-RADS in textual mammography reports

Automated classification of BI-RADS in textual mammography reports

Analysis of produce recognition system with taxonomist's knowledge using computer vision and different classifiers

Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

Contact Info

Product

Resources

About