Traditional Classification Algorithms Research Articles

Detection and classification of wilting status in leaf images based on VGG16 with EfficientNet V3 algorithm

The aim of this paper is to explore the importance of leaf wilting status detection and classification in agriculture to meet the demand for monitoring and diagnosing plant growth conditions. By comparing the performance of the traditional VGG16 image classification algorithm and the popular EfficientNet V3 algorithm in leaf image wilting status detection and classification, it is found that EfficientNet V3 has faster convergence speed and higher accuracy. As the model training process proceeds, both algorithms show a trend of gradual convergence of Loss and Accuracy and increasing accuracy. The best training results show that VGG16 reaches a minimum loss of 0.288 and a maximum accuracy of 96% at the 19th epoch, while EfficientNet V3 reaches a minimum loss of 0.331 and a maximum accuracy of 97.5% at the 20th epoch. These findings reveal that EfficientNet V3 has a better performance in leaf wilting status detection, which provides a more accurate and efficient means of plant health monitoring for agricultural production and is of great research significance.

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

New ensemble learning algorithm based on classification certainty and semantic correlation

A new bootstrap-aggregating (bagging) ensemble learning algorithm is proposed based on classification certainty and semantic correlation to improve the classification accuracy of ensemble learning. First, two predetermined thresholds are introduced to construct the long and short-text sample subsets, and different deep learning methods are compared to construct the optimal base classifier groups for each sample subsets. Then, the random sampling method employed in traditional bagging classification algorithms is improved, and a threshold group based random sampling method is proposed to obtain long and short training sample subsets of each iteration. Finally, the sample classification certainty of the base classifiers for different categories is defined, and the semantic correlation information is integrated with the traditional weighted voting classifier ensemble method to avoid the loss of important information during the sampling process. The experimental results on multiple datasets demonstrate that the algorithm significantly improves text classification accuracy and outperforms typical deep learning algorithms. The proposed algorithm achieves the improvements of approximately 0.082, 0.061 and 0.019 on CNews dataset when the F1 measurement is used over the traditional ensemble learning algorithms such as random forest, M_ADA_A_SMV and CNN_SVM_LR. Moreover, it achieves the best F1 values of 0.995, 0.985, and 0.989 on the datasets of Spam, CNews, and SogouCS datasets, respectively, when compared with the ensemble learning algorithms using different base classifiers.

Application of computer data model in the characteristics and evolution of ceramic decorative painting

With the rapid development of computer technology, computer information technology has made intelligent transformation to all industries. It is particularly important to optimize the computerization of ceramic painting. This paper analyzes the development of ceramic decorative painting and the basic principle of computer data model, determines the method of combining ceramic decorative painting with computer data model, and establishes a computer data model for feature extraction of ceramic decorative painting. And 1015 images of self-made ceramic decorative paintings are applied to carry out simulation analysis. The results show that compared with the traditional classification algorithm based on non deep learning and the partial classification algorithm based on deep learning, the average F1 value of the hybrid neural network model is 96.4%, which is 1.5% higher than the best traditional model. This shows that the designed computer data model for feature extraction of ceramic decorative painting has a good effect on the classification and feature recognition of ceramic painting. This has laid a certain foundation for the computerized optimization of ceramic decorative painting and provided a good support for the healthy development of ceramic decorative painting in the information age.

Analysis and Benchmarking of feature reduction for classification under computational constraints

Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully review, analyze, and identify the feature reduction methods that have low costs/overheads in terms of time and memory. Then, we evaluate the identified reduction methods in terms of their impact on the accuracy, precision, time, and memory costs of traditional classification algorithms. Specifically, we focus on the least resource intensive feature reduction methods that are available in Scikit-Learn library. Since our goal is to identify the best performing low-cost reduction methods, we do not consider complex expensive reduction algorithms in this study. In our evaluation, we find that at quadratic-scale feature reduction, the classification algorithms achieve the best trade-off among competitive performance metrics. Results show that the overall training times are reduced 61%, the model sizes are reduced 6×, and accuracy scores increase 25% compared to the baselines on average with quadratic scale reduction.

A post-processing framework for class-imbalanced learning in a transductive setting

Traditional classification tasks suffer from the class-imbalanced problem, where some classes far outnumber others. To address this issue, existing class-imbalanced learning (CIL) methods either preprocess class-imbalanced datasets or adapt traditional classification algorithms to the imbalanced class distribution. Inspired by the idea of transductive learning, we propose a post-processing framework called PPF for CIL. Distinct from existing CIL methods, PPF directly adjusts the predicted labels of test data to fit the imbalanced class distribution. Specifically, we relabel some test data according to their prediction probabilities so that the class proportion of test data is close to that of training data. The underlying assumption is that training and test data, drawn independently from one data space, should obey the same class distribution. Furthermore, we propose a Compact Prototype-based Nearest Neighbor (CPNN) algorithm to assist the original classifier with the adjustment. Instead of training a classifier, CPNN classifies test data according to their distances to a set of prototypes estimated on labeled data. Thus, it is computationally simple and relatively robust to class imbalance. As a general framework, PPF can be easily applied to both traditional classification and CIL algorithms. To validate the effectiveness of the proposed method, we conducted extensive experiments on a variety of class-imbalanced datasets, using SVM and C4.5 as the original classifiers, respectively. Measured by F-measure, G-mean, and AUC, both PPF-SVM and PPF-C4.5 outperform 10 state-of-the-art CIL algorithms. Additionally, PPF further improved their performances when applied to 10 CIL algorithms.

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

Dak Lak is a province in the Central Highlands region of Vietnam, with a large area of forests and forestry land. However, the forest cover has changed dramatically in recent times due to the influence of human activities and climate change. This article presents the results of assessing forest cover changes in Dak Lak province from Landsat satellite image data for the period 2000 – 2020. Three Landsat satellite image scenes, including Landsat 5 TM images taken in March 2000 and February 2010 and Landsat 8 OLI image taken in February 2020 are used to classify forest cover. Three common machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and the traditional maximum likelihood classification algorithm are used to classify forest cover in the study area, thereby choosing the method with the highest accuracy. The results show that the RF algorithm has the highest accuracy in classifying forest cover from multi-temporal Landsat images by comparing the overall accuracy value and the Kappa coefficient. The obtained results are used to build forest cover change maps in the period 2000 - 2010, 2010 - 2020 and 2000 - 2020. The results received in the study provide information to help managers in monitoring and protecting forest resources.

An Image Classification Method of Unbalanced Ship Coating Defects Based on DCCVAE-ACWGAN-GP

Affected by the improper operation of the workers, environmental changes during drying and curing or the quality of the paint itself, diverse defects are produced during the process of ship painting. The traditional defect recognition method relies on expert knowledge or experience to detect defects, which is not conducive to ensuring the effectiveness of defect recognition. Therefore, this paper proposes an image generation and recognition model which is suitable for small samples. Based on a deep convolutional neural network (DCNN), the model combines a conditional variational autoencoder (DCCVAE) and auxiliary conditional Wasserstein GAN with gradient penalty (ACWGAN-GP) to gradually expand and generate various coating defect images for solving the overfitting problem due to unbalanced data. The DCNN model is trained based on newly generated image data and original image data so as to build a coating defect image classification model suitable for small samples, which is conducive to improving classification performance. The experimental results showed that our proposed model can achieve up to 92.54% accuracy, an F-score of 88.33%, and a G mean value of 91.93%. Compared with traditional data enhancement methods and classification algorithms, our proposed model can identify various defects in the ship painting process more accurately and consistently, which can provide effective theoretical and technical support for ship painting defect detection and has significant engineering research value and application prospects.

A building electrical system fault diagnosis method based on random forest optimized by improved sparrow search algorithm

Addressing the problems of manual dependence and low accuracy of traditional building electrical system fault diagnosis, this paper proposes a novel method, which is based on random forest (RF) optimized by improved sparrow search algorithm (ISSA-RF). Firstly, the method utilizes a fault collection platform to acquire raw signals of various faults. Secondly, the features of these signals are extracted by time-domain and frequency-domain analysis. Furthermore, principal component analysis is employed to reduce the dimensionality of the extracted features. Finally, the reduced features are input into ISSA-RF for classification. In ISSA-RF, the ISSA is used to optimize the parameters of the RF. The parameters for ISSA optimization are n_estimators and min_samples_leaf. In this case, the accuracy of the proposed method can reach 98.61% through validation experiment. In addition, the proposed method also exhibits superior performance compared with traditional fault classification algorithms and the latest building electrical fault diagnosis algorithms.

Deep learning approach for detection of unfavorable driving state based on multiple phase synchronization between multi-channel EEG signals

In recent years, electroencephalogram (EEG) signals have gained significant popularity in the detection and recognition of drivers' unfavorable driving states (UDS) and related aspects. However, the methods that rely on traditional EEG features and evaluate different electrodes or brain regions separately have somewhat limited the further development of recognition effectiveness. To address this, this study analyzes the phase relationships between electrodes in EEG signals to reveal the functional networks and connectivity patterns within the brain. Three phase synchronization (PS) methods, namely phase locking value (PLV), corrected imaginary phase locking value (CIPLV), and weighted phase lag index (WPLI), are used to construct connectivity matrices and networks in six classic sub-rhythmic bands. Additionally, a 2D convolutional neural network (CNN) with fewer learning parameters is designed to automatically learn high-level abstractions and the most discriminative features, thereby avoiding the laborious and subjective process of manual feature extraction. The proposed method has been compared with traditional classification algorithms using a leave-one-out cross-validation strategy. The comparative results indicate that our proposed approach, which combines WPLI phase synchronization in the gamma1 sub-rhythmic band with a pyramid-structured CNN model, achieves significantly higher accuracy in recognizing drivers' UDS than other conventional methods through a one-way analysis of variance (ANOVA) followed by post hoc multiple comparisons with Tukey analysis. Furthermore, our proposed 2D-CNN model contains fewer parameters, making it more competitive for further practical applications in real-world driving scenarios.

Open set classification of sound event

Sound is one of the primary forms of sensory information that we use to perceive our surroundings. Usually, a sound event is a sequence of an audio clip obtained from an action. The action can be rhythm patterns, music genre, people speaking for a few seconds, etc. The sound event classification address distinguishes what kind of audio clip it is from the given audio sequence. Nowadays, it is a common issue to solve in the following pipeline: audio pre-processing→perceptual feature extraction→classification algorithm. In this paper, we improve the traditional sound event classification algorithm to identify unknown sound events by using the deep learning method. The compact cluster structure in the feature space for known classes helps recognize unknown classes by allowing large room to locate unknown samples in the embedded feature space. Based on this concept, we applied center loss and supervised contrastive loss to optimize the model. The center loss tries to minimize the intra- class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class features from one another. In addition, we explored the performance of self-supervised learning in detecting unknown sound events. The experimental results demonstrate that our proposed open-set sound event classification algorithm and self-supervised learning approach achieve sustained performance improvements in various datasets.

A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion

Abstract This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization of resources. To address the challenges of existing resource classification, the study proposes a label generation classification algorithm with multi-feature fusion. The algorithm incorporates TF-IDF and location information weights based on TextRank to generate labels containing corpus and location information. The performance of the algorithm is tested and simulation experiments are conducted. The study results show that the mean value of the label generation classification algorithm after multi-feature fusion reaches 0.234, 0.6219, and 0.3632 in terms of accuracy, recall, and F-value, respectively. In the simulation experiments, the algorithm achieves a classification speed of 90.33% faster than that of the traditional single-feature classification algorithm, and the classification error rate is no more than 10%. The label generation classification algorithm has a significant advantage in overall performance and effectively solves the problem of classifying university English teaching resources. This finding is of great significance for improving the utilization of teaching resources and optimizing the management of teaching resources.

Identification of Disease Type of Tobacco Leaves Based on Near Infrared Spectroscopy and Convolutional Neural Network

It is important to identify the types of tobacco diseases accurately and take effective control measures in time to improve the efficiency of tobacco planting. In this paper, a hand-held nearinfrared spectrometer was used to collect the spectral data of different types of tobacco disease samples. The training models were established via convolutional neural network algorithm. Meanwhile, the traditional classification algorithms support vector machine and back propagation neural network were also compared. The results showed that the prediction accuracy of convolutional neural network algorithm was the highest and the overall performance of the model was the best. The rapid detection method based on a hand-held near-infrared spectrometer and convolutional neural network algorithm could identify tobacco leaf disease species efficiently, non-destructively, quickly and accurately, which provided a new technical reference for tobacco leaf disease species detection and identification.

Gaussian distribution resampling via Chebyshev distance for food computing

The problem of data imbalance often occurs in the real-world food domain. Traditional classification algorithms are prone to overfitting on imbalanced datasets, and the decision surface will be biased toward majority-class samples, making it difficult to identify minority-class samples. Although previous resampling techniques can deal with the imbalance problem by balancing the dataset, they may produce class overlap because the anchor samples are not appropriately selected and the generated dataset does not conform to the original distribution. This paper proposes an adaptive resampling technique based on Gaussian distribution oversampling combined with random undersampling (GDRS) to address the abovementioned problems. The technique is based on the Chebyshev distance combining the weight information of the minority-class samples to select a suitable anchor sample. A new dataset conforming to the original distribution is generated in the form of a Gaussian distribution around the anchor sample. Then the random undersampling technique is combined to reduce the possibility of overfitting. The technique is applied to five UCI datasets and compared with seven imbalanced learning methods. The experimental results demonstrate that our method GDRS yields optimal performance. We also validate the effectiveness of our method in dealing with real dairy datasets with different imbalance ratios, which is promising for application in the food field.

Column generation-based prototype learning for optimizing area under the receiver operating characteristic curve

The traditional classification algorithms focus on the maximization of classification accuracy which might lead to poor performance in practice by forcing classifiers to overfit to the majority class. In order to overcome this issue, various approaches focus on the optimization of alternative loss functions such as the Area Under the Curve (AUC). AUC is a Receiver Operating Characteristics (ROC) metric that has been widely used to measure classification performance, especially when there are class imbalances. In this work, we propose a column generation (CG)-based algorithm called Ranking-CG, which learns a model, similar to the popular Ranking SVM, through approximate maximization of the AUC. Unlike the Ranking SVM, our algorithm utilizes a column generation method that iteratively adds features to control the model complexity effectively working as an internal feature selection procedure. Our experiments show that column generation can be an important tool to prevent overfitting. We extend the Ranking-CG by proposing a prototype generation method, denoted by Ranking-CG Prototype, that constructs reference points by solving a non-linear optimization problem. Based on the extensive experiments conducted on 74 binary classification problems, the Ranking-CG Prototype yields the best average test AUC among all competing methods by using significantly few features than other benchmarks.

Adaptive Boosting Method for Mitigating Ethnicity and Age Group Unfairness

Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be unfair to certain demographic groups, perpetuating socially induced biases. Therefore, this paper deals with a common unfairness problem, unequal quality of service, that appears in classification when age and ethnicity groups are used. To tackle this issue, we propose an adaptive boosting algorithm that aims to mitigate the existing unfairness in data. The proposed method is based on the AdaBoost algorithm but incorporates fairness in the calculation of the instance’s weight with the goal of making the prediction as good as possible for all ages and ethnicities. The results show that the proposed method increases the fairness of age and ethnicity groups while maintaining good overall quality compared to traditional classification algorithms. The proposed method achieves the best accuracy in almost every sensitive feature group. Based on the extensive analysis of the results, we found that when it comes to ethnicity, interestingly, White people are likely to be incorrectly classified as not being heroin users, whereas other groups are likely to be incorrectly classified as heroin users.

Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.Graphical

TransHSI: A Hybrid CNN-Transformer Method for Disjoint Sample-Based Hyperspectral Image Classification

Hyperspectral images’ (HSIs) classification research has seen significant progress with the use of convolutional neural networks (CNNs) and Transformer blocks. However, these studies primarily incorporated Transformer blocks at the end of their network architectures. Due to significant differences between the spectral and spatial features in HSIs, the extraction of both global and local spectral–spatial features remains incomplete. To address this challenge, this paper introduces a novel method called TransHSI. This method incorporates a new spectral–spatial feature extraction module that leverages 3D CNNs to fuse Transformer to extract the local and global spectral features of HSIs, then combining 2D CNNs and Transformer to capture the local and global spatial features of HSIs comprehensively. Furthermore, a fusion module is proposed, which not only integrates the learned shallow and deep features of HSIs but also applies a semantic tokenizer to transform the fused features, enhancing the discriminative power of the features. This paper conducts experiments on three public datasets: Indian Pines, Pavia University, and Data Fusion Contest 2018. The training and test sets are selected based on a disjoint sampling strategy. We perform a comparative analysis with 11 traditional and advanced HSI classification algorithms. The experimental results demonstrate that the proposed method, TransHSI algorithm, achieves the highest overall accuracies and kappa coefficients, indicating a competitive performance.

PMFCC Features for Music Classification Using the Modified KNN Algorithm

From ancient period Music is a integral part of human life. People prefered to listen Music while relaxing. All over the world nowadays music is used as supporting medication for healing mental illness and many other diseases. The origin of Indian Music is Indian Classical Raga, having a melodious combination of Rhythm and notes. There are various users like music-composers, e-learners, music therapists are frequently retrieving Indian classical Raga based music. The vast retrieval of Raga based music made it necessary to classify Indian music on Raga. This paper proposes an a new algorithm to classify an Indian Music using Raga information which further will be useful for song recommendation, personalizing collection, and musicologists for various purpose.
 In this paper, the combined Pitch and MFCC based PMFCC features are extracted and processed by Modified K Nearest Neighbor algorithm. The performance of PMFCC and Pitch Class Distribution features is compared using traditional machine learning classification algorithms and Modified Variant K Nearest Neighbor (MVKNN). The PMFCC features outperformed with the Modified KNN algorithm. The accuracy of PMFCC features with Modified KNN algorithm is found 96.11% for our Our dataset and 93.65% for Compmusic standard dataset.

A Novel Semantic Segmentation Approach Using Improved SegNet and DSC in Remote Sensing Images

An improved SegNet semantic segmentation model is proposed to address the issue of traditional classification algorithms and shallow learning algorithms not being suitable for extracting information from high-resolution remote sensing images. During the research process, space remote sensing images obtained from the GF-1 satellite were used as the data source. In order to improve the operational efficiency of the encoding network, the pooling layer in the encoding network is removed and the ordinary convolutional layer is replaced with a depth-wise separable convolution. By decoding the last layer of the network to obtain the reshaped output results, and then calculating the probability of each classification using a Softmax classifier, the classification of pixels can be achieved. The output result of the classifier is the final result of the remote sensing image semantic segmentation model. The results showed that the proposed algorithm had the highest Kappa coefficient of 0.9531, indicating good classification performance.