Performance Comparison of Feature Selection Methods

Phyu, Thu Zar; Oo, Nyein Nyein

doi:10.1051/matecconf/20164206002

Cited by 34 publications

(22 citation statements)

References 9 publications

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“…Filter-based is a simple FS method, yet effective for all types of datasets. In addition, Filter is independence of classifiers and more scalable comparing to other FS methods [17]. In this study, we propose comparative approach of experiement of a proposed feature selection method to three existing baseline methods.…”

Section: Feature Selection Methods and Dominant Setmentioning

confidence: 99%

Study on Dominant Factor for Academic Performance Prediction using Feature Selection Methods

Sokkhey¹,

Okazaki²

2020

IJACSA

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All educational institutions always try to investigate the learning behaviors of students and give early prediction toward student's outcomes for interventing and improving their learning performance. Educational data mining (EDM) offers various effective prediction models to predict student performance. Simultaneously, feature selection (FS) is a method of EDM that is utilized to determine the dominant factors that are needed and sufficient for the target concept. FS method extracts high-quality data that reduce the complexity of the prediction task that can increase the robustness of decision rule. In this paper, we provide a comparative study of feature selection methods for determining dominant factors that highly affect classification performance and improve the performance of prediction models. A new feature selection CHIMI based on ranked vector score is proposed. Analysis of feature sets of each FS method to get the dominant set is executed. The experimental results show that by using the dominant set of the proposed CHIMI method, the classification performance of the proposed models is significantly improved.

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Section: Feature Selection Methods and Dominant Setmentioning

confidence: 99%

Study on Dominant Factor for Academic Performance Prediction using Feature Selection Methods

Sokkhey¹,

Okazaki²

2020

IJACSA

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“…The method seeks to identify and delete as much as possible irreparable and redundant information. It simplifies the data set both in size and in complexity of understanding, which leads to simpler and faster classification algorithms, better problem comprehension and reduced storage requirements (Eiras‐Franco et al, ; Phyu & Oo, ).…”

Section: Methodsmentioning

confidence: 99%

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Liang

et al. 2019

Transbound Emerg Dis

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African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator‐Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%–84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision‐makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

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“…The way Pearson correlation coefficient ρ dealings the strength of the relationship between two features to find the similarity between of them, is based on value which the giving a value between +1 and -1, where 1 indicates positive, 0 indicates no correlation and -1 is negative correlation [18]. [18]. The effectiveness of those two feature selection techniques is evaluate in classification phase as mentioned earlier.…”

Section: Methodsmentioning

confidence: 99%

Comparison of feature selection techniques in classifying stroke documents

Rafei

Hassan

Saedudin

et al. 2019

IJEECS

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<span>The amount of digital biomedical literature grows that make most of the researchers facing the difficulties to manage and retrieve the required information from the Internet because this task is very challenging. The application of text classification on biomedical literature is one of the solutions in order to solve problem that have been faced by researchers but managing the high dimensionality of data being a common issue on text classification. Therefore, the aim of this research is to compare the techniques that could be used to select the relevant features for classifying biomedical text abstracts. This research focus on Pearson’s Correlation and Information Gain as feature selection techniques for reducing the high dimensionality of data. Towards this effort, we conduct and evaluate several experiments using 100 abstract of stroke documents that retrieved from PubMed database as datasets. This dataset underwent the text pre-processing that is crucial before proceed to feature selection phase. Features selection phase is involving Information Gain and Pearson Correlation technique. Support Vector Machine classifier is used in order to evaluate and compare the effectiveness of two feature selection techniques. For this dataset, Information Gain has outperformed Pearson’s Correlation by 3.3%. This research tends to extract the meaningful features from a subset of stroke documents that can be used for various application especially in diagnose the stroke disease.</span>

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Performance Comparison of Feature Selection Methods

Cited by 34 publications

References 9 publications

Study on Dominant Factor for Academic Performance Prediction using Feature Selection Methods

Study on Dominant Factor for Academic Performance Prediction using Feature Selection Methods

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Comparison of feature selection techniques in classifying stroke documents

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