Exploiting RLPI for Sentiment Analysis on Movie Reviews

Hs, Darshan; Shankar, Aditya Ravi; Harish, B. S.; Kumar, Keerthi H M

doi:10.12720/jait.10.1.14-19

Cited by 19 publications

(1 citation statement)

References 24 publications

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“…Moreover, the two other feature selection methods introduced different ranked features in their selected subsets. This is because different feature selection methods use different statistical criteria to calculate the importance of each feature and its relevance to the class label [78,79]. Further, from table 2, only colour and morphological characteristics are selected as the top 10 most informative attributes.…”

Section: B Results Of Feature Selection Methodsmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M1 and LogitBoost algorithms was evaluated to enhance the plant classification performance of four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machine (SVM). Four feature filtering techniques including Correlationbased Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR), and OneR were applied to the imageextracted features and 10 of the most significant features were selected and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M1 and LogitBoost algorithms improved the classification accuracy. Regarding the performance values, the LogitBoost-IG-RF structure, which provided a classification accuracy of 99.58%, a kappa (k) of 0.9948, and a Root Mean Squared Error (RMSE) of 0.0688 on training dataset, was selected as the most appropriate classifier for plant discrimination in peanut fields. The accuracy, k, and RMSE criteria of this combination on test dataset were 95.00%, 0.9375, and 0.1591, respectively. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

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Section: B Results Of Feature Selection Methodsmentioning

confidence: 99%