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Improvement in classification algorithms through Model Stacking with the Consideration of their Correlation

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第3期10卷 463-475页

作者： Azam, Muhammad Ahmed, Tanvir Hashmi, M. Usman Ahmad, Rehan Manan, Abdul Adrees, Muhammad Sabah, Fahad Super Coll Dept CS & IT Lahore Pakistan Univ Lahore Dept Comp Sci Lahore Pakistan

In this research we analyzed the performance of some well-known classification algorithms in terms of their accuracy and proposed a methodology for model stacking on the basis of their correlation which improves the accuracy of these algorithms. We selected;Support Vector Machines (svm), Naive Bayes (nb), k-Nearest Neighbors (knn), Generalized Linear Model (glm), Latent Discriminant Analysis (Ida), gbm, Recursive Partitioning and Regression Trees (rpart), rda, Neural Networks (nnet) and Conditional Inference Trees (ctree) in our research and preformed analyses on three textual datasets of different sizes;Scopus 50,000 instances, IMDB Movie Reviews having 10,000 instances, Amazon Products Reviews having 1000 instances and Yelp dataset having 1000 instances. We used R-Studio for performing experiments. Results show that the performance of all algorithms increased at Meta level. Neural Networks achieved the best results with more than 25% improvement at Meta-Level and outperformed the other evaluated methods with an accuracy of 95.66%, and altogether our model gives far better results than individual algorithms' performance.

关键词： classification algorithms model stacking correlation k-nearest neighbor pre-processing meta classifiers

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Investigating the Environmental and the Energy Saving Behavior among School Principals through classification algorithms

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED 2022年第2期11卷 449-461页

作者： Ntanos, Stamatios Kyriakopoulos, Grigorios L. Anagnostopoulos, Theodoros Xanthopoulos, Theodoros Kytagias, Christos Drosos, Dimitrios Univ West Attica Sch Business Econ & Social Sci Dept Business Adm 250 Thivon & P Ralli Str Egaleo 12244 Greece Natl Tech Univ Athens Sch Elect & Comp Engn Elect Power Div Photometry Lab Athens 15780 Greece Univ West Attica Dept Business Adm DigiTDSSLab 250 Thivon & P Ralli Str Egaleo 12244 Greece

Buildings are a significant energy consumption point since they account for 40% of the total energy demand and around 1/3 of greenhouse gas emissions. Energy-saving measures applied in the residential sector have led to a reduction in energy consumption during the last decade. On the contrary, such measures have not been widely applied in school buildings, although education is the second-largest energy consumer in the service sector. This paper aims to assess school principals' perceptions concerning energy saving and the environment since they are responsible for promoting energy-saving measures and investments and inspiring students and school personnel towards environmentally friendly behavior. Using survey data from Greek schools, we applied predictive classification models to locate the most critical variables that drive principals' perceptions of energy upgrading and energy-saving actions at school. Results revealed that the positive environmental perceptions of principals, the level of knowledge on Renewable Energy Sources (RES) and the active energy-saving behavior are related to energy-saving actions and energy upgrading in school environment. Furthermore, the creation of more RES oriented courses is related to positive energy-saving behavior and actions. Thus, emphasis should be put on educating and informing the school principals concerning RES technologies and energy-saving options since they are critical players in applying energy-saving measures in school buildings.

关键词： environmental behavior school environment renewable energy energy-saving school building classification algorithms

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The classification algorithms to support the management of the patient with femur fracture

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 1页

作者： Scala, Arianna Trunfio, Teresa Angela Improta, Giovanni Univ Naples Federico II Dept Publ Hlth Naples Italy Univ Naples Federico II Dept Adv Biomed Sci Naples Italy Univ Naples Federico II Interdept Res Ctr Management & Innovat Healthcare Naples Italy

Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient's length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners.

关键词： Modelling Machine learning classification algorithms Femur fracture Biomedical data analysis

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Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages

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GISCIENCE & REMOTE SENSING 2023年第1期60卷 Article: 2170479页

作者： Zhang, Ning Chai, Xiujuan Li, Niwen Zhang, Jianhua Sun, Tan Chinese Acad Agr Sci Agr Informat Inst Beijing Peoples R China Minist Agr & Rural Affairs Key Lab Agr Big Data Beijing Peoples R China Beijing Forestry Univ Forestry Coll Precis Forestry Key Lab Beijing Beijing Peoples R China Swedish Univ Agr Sci Dept Forest Resource Management Umea Sweden

As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (II) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (III) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Consi

关键词： Pine wilt disease unmanned aerial vehicles optical imagery classification algorithms

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QUEST: Eliminating Online Supervised Learning for Efficient classification algorithms

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SENSORS 2016年第10期16卷 1629-1629页

作者： Zwartjes, Ardjan Havinga, Paul J. M. Smit, Gerard J. M. Hurink, Johann L. Univ Twente Fac Elect Engn Math & Comp Sci NL-7500 AE Enschede Netherlands

In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

关键词： wireless sensor networks unsupervised learning classification algorithms Naive Bayes semi-supervised learning adaptive

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The Effect of Preprocessing Techniques, Applied to Numeric Features, on classification algorithms' Performance

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DATA 2021年第2期6卷 11-11页

作者： Alshdaifat, Esra'a Alshdaifat, Doa'a Alsarhan, Ayoub Hussein, Fairouz El-Salhi, Subhieh Moh'd Faraj S. Hashemite Univ Fac Prince Al Hussein Bin Abdallah II Informat Te Dept Comp Informat Syst POB 330127 Zarqa 13133 Jordan

It is recognized that the performance of any prediction model is a function of several factors. One of the most significant factors is the adopted preprocessing techniques. In other words, preprocessing is an essential process to generate an effective and efficient classification model. This paper investigates the impact of the most widely used preprocessing techniques, with respect to numerical features, on the performance of classification algorithms. The effect of combining various normalization techniques and handling missing values strategies is assessed on eighteen benchmark datasets using two well-known classification algorithms and adopting different performance evaluation metrics and statistical significance tests. According to the reported experimental results, the impact of the adopted preprocessing techniques varies from one classification algorithm to another. In addition, a statistically significant difference between the considered data preprocessing techniques is demonstrated.

关键词： preprocessing classification algorithms normalization missing values classification performance data cleaning

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Cocoa Pods Diseases Detection by MobileNet Confluence and classification algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 344-352页

作者： Mamadou, Diarra Ayikpa, Kacoutchy Jean Ballo, Abou Bakary Kouassi, Brou Medard Univ Felix Houphouet Boigny LaMI Abidjan Cote Ivoire Univ Virtuelle Cote Divoire UREN Abidjan Cote Ivoire Univ Bourgogne ImViA Dijon France Univ Peleforo Gon Coulibaly LMI Korhogo Cote Ivoire

cultivation is of immense importance to the people of Cote d'Ivoire. However, this culture is experiencing significant challenges due to diseases spread by various agents such as bacteria, viruses, and fungi, which cause considerable economic losses. Currently, the methods available to detect these cocoa diseases force farmers to seek the expertise of agronomists for visual inspections and diagnostics, a laborious and complex process. In the search for solutions, many studies have opted for using convolutional neural networks (CNNs) to identify diseases in cocoa pods. However, an essential advance is to develop hybrid approaches that combine the advantages of a CNN with sophisticated classification algorithms. This research stands out for its innovative contribution, combining MobileNetV2, a convolutional neural network architecture, with algorithms, such as Logistic Regression (LR), K Nearest Neighbors (KNN), Support Vector Machines (SVM), XGBoost, and Random Forest. The study was conducted in two distinct phases. First, each algorithm was evaluated individually, and then performance was measured when MobileNetV2 was merged with the algorithms mentioned. These hybrid approaches complement and amplify MobileNetV2's capabilities. To do so, they draw on MobileNetV2's inherent capabilities to extract key features and enhance information quality. By combining this expertise with the classification methods of these other models, hybrid approaches outperform individual techniques. Accuracy rates range from 72.4% to 86.04%.This performance amplitude underlines the effectiveness of the synergy between the extraction characteristics of MobileNetV2 and the classification skills of other algorithms.

关键词： Cocoa pods diseases MobileNetV2 classification algorithms machine learning hybrid method

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Developing A Predictive Model for Selecting Academic Track Via GPA by using classification algorithms: Saudi Universities as Case Study

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第5期14卷 371-377页

作者： Althubiti, Thamer Ahmed, Tarig M. Alassafi, Madini O. King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah 21589 Saudi Arabia

The main motivation of any educational institution is to provide quality education. Therefore, choosing an academic track can be clearly seen as an obstacle, for students and universities, which in turn led to imposing a mandatory preparatory year program in Saudi Arabia. One of the main objectives of the preparatory year is to help students discover the right academic track. Nevertheless, some students choose the wrong academic track which can be a stumbling block that may prevent their progress. According to the tremendous growth of using information technology, educational data mining technology (EDM) can be applied to discover useful patterns, unlike traditional data analysis methods. Most of the previous research focused on predicting the GPA after the students choose an academic track. On the contrary, our research focuses on using classification algorithms to develop a predictive model for advising students to select academic tracks via prediction of the GPA based on the preparatory year data at Saudi Universities. Then, compare classification algorithms to provide the most accurate prediction. The dataset was extracted from a Saudi university containing preparatory year data for 2363 students. This work was carried out using five classification algorithms: Gradient Boosting(GB), K-Nearest Neighbors (kNN), Logistic Regression (LG), Neural Network(NN) and Random Forest(RF). The results showed the superiority of the Logistic Regression algorithm in terms of accuracy over the other algorithms. Future work could add behavioral characteristics of students and use other algorithms to provide better accuracy.

关键词： Data mining educational data mining classification algorithms logistic regression neural networks gradient boosting k-nearest neighbors predicting students' performance

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A Comparative Study on classification of Sleep Stage Based on EEG Signals Using Feature Selection and classification algorithms

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JOURNAL OF MEDICAL SYSTEMS 2014年第3期38卷 18-18页

作者： Sen, Baha Peker, Musa Cavusoglu, Abdullah Celebi, Fatih V. Yildirim Beyazit Univ Dept Comp Engn Ankara Turkey Karabuk Univ Dept Comp Engn TR-78050 Karabuk Turkey

Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR);fast correlation based feature selection (FCBF);ReliefF;t-test;and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF);feed-forward neural network (FFNN);decision tree (DT);support vector machine (SVM);and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

关键词： EEG signals classification algorithms Feature selection algorithms classification of sleep stage

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Influence of Boundary Extraction Operators on Boundary Shape classification algorithms Based on Complex Networks†

Influence of Boundary Extraction Operators on Boundary Shape...

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Chinese Automation Congress (CAC)

作者： Sheng-Qiao Bao Huibin Xu Hao Wang Jie Liu School of Mathematics and Computer Science Wuhan Textile University Wuhan School of Mathematics and Computer Science Wuhan Textile University Wuhan P R China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

As an important visual attribute for characterizing image objects, shape is always be investigated by researchers in pattern recognition and image analysis fields. It provides the most relevant information about objects for identification and classification tasks. By using the classical first-order and second-order operator algorithms, this brief paper extracts the shape boundaries of contour objects in several image sets. Then, generating explanatory variables based on the newly proposed complex network analysis method of the associated dynamic evolution boundaries network was carried out. Differences in the construction of high-dimensional feature interpretation vectors of boundary shapes based on network analysis are investigated. Our research shows that: the shape contour fingerprint vectors constructed by contour network construction algorithms based on different boundary extraction algorithms, can effectively represent the internal geometric figures of contour graphics features. The extracted digital feature vectors can be successfully used for graph classification tasks on the Etu10Silhouette database and Kimia216 database. There are some differences in CPU costs and classification accuracy. For the Etu10Silhouette database, it is recommended to use Sobel operator to extract the contours of the image shape, then use the algorithm mentioned in this paper to extract the fingerprint vector, combined with the KNN classification algorithm, one can get the best classification accuracy with almost the least time overhead. For the Kimia216 database, it is recommended to use the Laplacian operator to extract the contours of the image shape, with the boundary network fingerprint vector extraction algorithm, and combined with the LDA classification algorithm, one can obtain the best classification accuracy with almost the least time overhead. The comparative study on the classification algorithm based on deep learning method based on CNN also showed that, based on the g

关键词： Image edge detection Shape classification algorithms Complex networks Fingerprint recognition Feature extraction Low-pass filters

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