An alternative framework for univariate filter based feature selection for text categorization

Detection of plant volatile organic compounds (VOCs) enables monitoring of pests and diseases in agriculture. We previously revealed that a localized surface plasmon resonance (LSPR) sensor coated with a molecularly imprinted sol-gel (MISG) can be used for cis-jasmone vapor detection. Although the selectivity of the LSPR sensor was enhanced by the MISG coating, its sensitivity was decreased. Here, gold nanoparticles (AuNPs) were doped in the MISG to enhance the sensitivity of the LSPR sensor through hot spot generation. The size and amount of AuNPs added to the MISG were investigated and optimized. The sensor coated with the MISG containing 20 μL of 30 nm AuNPs exhibited higher sensitivity than that of the sensors coated with other films. Furthermore, an optical multichannel sensor platform containing different channels that were bare and coated with four types of MISGs was developed to detect plant VOCs in single and binary mixtures. Linear discriminant analysis, k-nearest neighbor (KNN), and naïve Bayes classifier approaches were used to establish plant VOC identification models. The results indicated that the KNN model had good potential to identify plant VOCs quickly and efficiently (96.03%). This study demonstrated that an LSPR sensor array coated with a AuNP-embedded MISG combined with a pattern recognition approach can be used for plant VOC detection and identification. This research is expected to provide useful technologies for agricultural applications.

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“…Because of its simple structure and ease of implementation, NBC has been widely used . More details on KNN and NBC can be found elsewhere. − …”

Section: Resultsmentioning

confidence: 99%

Plant Biomarker Recognition by Molecular Imprinting Based Localized Surface Plasmon Resonance Sensor Array: Performance Improvement by Enhanced Hotspot of Au Nanostructure

et al. 2018

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“…where N is the number of total samples. a ij is the number of samples containing feature t i in the C j class, and b ij is the number of samples not containing feature t i in the C j class [36]. c ij is the number of samples with feature t i that is not in the C j class.…”

Section: Chi-square Algorithmmentioning

confidence: 99%

A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

Demir

Siddique

Alswaitti

et al. 2022

JPM

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Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

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“…, in which n denotes the number of selected features and w is the set of all term weights [17,18]. In the whole process, the assignment of term weight is a vital step because the weight demonstrates the importance of a specific term and the contribution made by this term in classifying different kinds of texts [3,14,19].…”

Section: Introductionmentioning

confidence: 99%

An Improved Term Weighting Method Based on Relevance Frequency for Text Classification

Zhong

et al. 2021

Preprint

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As a vital step of text classification (TC) task, the assignment of term weight has a great influence on the performance of TC. Currently, masses of term weighting schemes can be utilized, such as term frequency-inverse documents frequency (TF-IDF) and term frequency-relevance frequency (TF-RF), and they are all consisted of local part (TF) and global part (e.g., IDF, RF). However, most of these schemes adopt the logarithmic processing on their respective global parts, and it is natural to consider whether the logarithmic processing apply to all these schemes or not. Actually, for a specific term weighting scheme, due to its different ratio of local weight and global weight resulting from logarithmic processing, it usually shows diverse text clasification results on different text sets, which presents poor robustness. To explore the influence of logarithmic processing imposed on the global weight on the classification result of term weighting schemes, TF-RF is selected as the representative because it can achieve a better performance among these schemes adopting logarithmic processing. Then, two propositions along with corresponding methods about the relation between TF part and RF part are proposed based on TF-RF. In addition, two groups of experiments are conducted on the two methods. The first group of experiments proves that one method (denoted as TF-ERF) is more helpful to the improvement than the other one (denoted as ETF-RF). The second group of experiments shows that TF-ERF not only ourperforms TF-RF but also obtains better performance than other existing term weighting schemes.

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An alternative framework for univariate filter based feature selection for text categorization

Cited by 36 publications

References 31 publications

Plant Biomarker Recognition by Molecular Imprinting Based Localized Surface Plasmon Resonance Sensor Array: Performance Improvement by Enhanced Hotspot of Au Nanostructure

Plant Biomarker Recognition by Molecular Imprinting Based Localized Surface Plasmon Resonance Sensor Array: Performance Improvement by Enhanced Hotspot of Au Nanostructure

A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

An Improved Term Weighting Method Based on Relevance Frequency for Text Classification

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