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Neural network model for classification algorithms and its application

25th Chinese Control Conference

作者： Ye Qian Zhejiang Univ Finance & Econ Sch Finance Hangzhou 310012 Peoples R China

ISBN: (纸本)9787810778022

Neural Network-based systems that allow the system,through an analysis of historical data to determine the relationship between account characteristics and the probability of default. The backpropagation algorithm - the multilayer feedforward network structure is described based on data with nine fiancial ratios from 81 firms listed in china, A simulation on network is made. Neural Network model for classification algorithms are established. By varying Network Parameters we demonstrate that LevenbergMarque training error is smallest among 4 learning algorithms and its performance is better. Increasing the number of hidden layer can result in minor improvement.

关键词： neural network classification algorithms financial ratio the multilayer feedforward network

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Evaluation of Ten Open-Source Eye-Movement classification algorithms in Simulated Surgical Scenarios

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IEEE ACCESS 2019年 7卷 161794-161804页

作者： Dalveren, Gonca Gokce Menekse Cagiltay, Nergiz Ercil Norwegian Univ Sci & Technol Dept Comp Sci N-2815 Gjovik Norway Atilim Univ Dept Informat Syst Engn TR-06830 Ankara Turkey Atilim Univ Dept Software Engn TR-06830 Ankara Turkey

Despite providing several insights into visual attention and evidence regarding certain brain states and psychological functions, classifying eye movements is a highly demanding process. Currently, there are several algorithms to classify eye movement events which use different approaches. However, to date, only a limited number of studies have assessed these algorithms under specific conditions, such as those required for surgical training programmes. This study presents an investigation of ten open-source eye-movement classification algorithms using the Eye Tribe eye-tracker. The algorithms were tested on the eye-movement records obtained from 23 surgical residents, who performed computer-based surgical simulation tasks under different hand conditions. The aim was to offer data for the improvement of surgical training programmes. According to the results, due to the different classification methods and default threshold values, the ten algorithms produced different results. Considering the fixation duration, the only common event for all of the investigated algorithms, the binocular-individual threshold (BIT) algorithm resulted in a different clustering compared to the other algorithms. Based on the other set of common events, three clusters were determined by eight algorithms (except BIT and event detection (ED)), distinguishing dispersion-based, velocity-based and modified versions of velocity-based algorithms. Accordingly, it was concluded that dispersion-based and velocity-based algorithms provided different results. Additionally, as it individually specifies the threshold values for the eye-movement data, when there is no consensus about the threshold values to be set, the BIT algorithm can be selected. Especially for such cases like simulation-based surgical skill-training, the use of individualised threshold values in the BIT algorithm can be more beneficial in classifying the raw eye data and thus evaluating the individual progress levels of trainees based

关键词： classification algorithms Surgery Clustering algorithms Heuristic algorithms Training Tracking Open source software Eye-movement classification algorithms eye-movement events eye-tracking

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Early prediction of ICU readmissions using classification algorithms

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COMPUTERS IN BIOLOGY AND MEDICINE 2020年第0期118卷 103636-000页

作者： Loreto, Melina Lisboa, Thiago Moreira, Viviane P. Univ Fed Rio Grande do Sul Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Hosp Clin Porto Alegre Programa Posgrad Ciencias Pneumol Porto Alegre RS Brazil Univ LaSalle Canoas RS Brazil

Context: Determining which patients are ready for discharge from an Intensive Care Unit (ICU) presents a huge challenge, as ICU readmissions are associated with several negative outcomes such as increased mortality, length of stay, and cost compared to those patients who are not readmitted during their hospital stay. For these reasons, enhancing risk stratification in order to identify patients at high risk of clinical deterioration might benefit and improve the outcomes of critically ill hospitalized patients. Existing work on predicting ICU readmissions relies on information available at the time of discharge, however, in order to be more useful and to prevent complications, predictions need to be made earlier. Goals: In this work, we investigate the hypothesis that the basal characteristics and information collected at the time of the patient's admission can enable accurate predictions of ICU readmission. Materials and Methods: We analyzed an anonymized dataset of 11,805 adult patients from three ICUs in a Brazilian university hospital. After excluding 1879 patients who died during their first ICU admission, our final dataset contained 9,926 patients. Of these, 658 patients (6.6%) had been readmitted to the ICU. The original dataset had 185 attributes, including demographics, length of stay prior to ICU admission, comorbidities, severity indexes, interventions, organ support care during ICU stay and laboratory results. The problem of predicting ICU readmissions was modeled as a binary classification task. We tested eight classification algorithms (including Bayesian algorithms, decision trees, rule-based, and ensemble methods) over different sets of attributes and evaluated their results based on six metrics. Results: Predictions made solely based on the attributes collected at the admission are highly accurate. Their quality in terms of prediction is no different from predictions made using the complete set of attributes for our dataset and for a subset of attri

关键词： ICU readmission Machine learning classification algorithms

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Coastal zone significant wave height prediction by supervised machine learning classification algorithms

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OCEAN ENGINEERING 2021年 221卷 108592-108592页

作者： Demetriou, Demetris Michailides, Constantine Papanastasiou, George Onoufriou, Toula Cyprus Univ Technol Dept Civil Engn & Geomat Limassol Cyprus VTT Vasiliko Ltd Larnax Cyprus

Explicit wave models and expensive sensor equipment capable of predicting and measuring wave parameters often carry a prohibitive computational and financial expense. To counter this, this paper proposes an alternative method for nowcasting coastal zone significant wave heights through the joint use of meteorological and structural data in the training of supervised machine learning models. In testing the hypothesis that structural data can improve model classification, artificial neural network and decision tree models were developed, trained and tested on field data recorded on a coastal jetty located in the southern coasts of Cyprus. A comprehensive investigation of the different models yields that the joint use of meteorological and structural features can improve classification performance, regardless of the network choice. It is also demonstrated that redundancy of training parameters could inject unwanted overfitting, reducing model generalization. To address this, a method for quantifying feature importance has been proposed by exploiting the nature of decision tree algorithms and the Gini impurity index, reaffirming that structural features do indeed benefit model classification. These results highlight the potential of tapping into the untapped pool of structural data for significant wave height prediction, paving the way for new research to be undertaken in this direction.

关键词： Machine learning Significant wave height prediction Neural networks classification algorithms Gini impurity index Decision tree

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Comparative study of classification algorithms for immunosignaturing data

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BMC BIOINFORMATICS 2012年第1期13卷 1-15页

作者： Kukreja, Muskan Johnston, Stephen Albert Stafford, Phillip Arizona State Univ Ctr Innovat Med Biodesign Inst Tempe AZ 85281 USA

Background: High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data. Results: We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to

关键词： Immunosignature Random peptide microarray Data mining classification algorithms Naive Bayes

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Comparison of classification algorithms for Detecting Typical Coastal Reclamation in Guangdong Province with Landsat 8 and Sentinel 2 Images

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REMOTE SENSING 2022年第2期14卷 385页

作者： Ai, Bin Huang, Ke Zhao, Jun Sun, Shaojie Jian, Zhuokai Liu, Xiaoding Sun Yat Sen Univ Sch Marine Sci Zhuhai 519082 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519080 Peoples R China Guangdong Prov Key Lab Marine Resources & Coastal Guangzhou 510275 Peoples R China Surveying & Mapping Inst Lands & Resource Dept Guangdong Prov Guangzhou 510663 Peoples R China Minist Nat Resources Key Lab Nat Resources Monitoring Trop Subtrop Are Guangzhou 510663 Peoples R China

Coastal reclamation in Guangdong Province is highly concentrated and is growing rapidly. However, intensive reclamation use has resulted in serious influence on the coastal ecosystem, directly and indirectly. The current conditions and spatial distribution of reclamations must be detected for coastal preservation and management using efficient technology. This study aims to find a suitable method and data to map reclamations accurately at a large scale. Pixel-based and object-oriented classification methods were applied in extracting the three typical types of coastal reclamation, namely, ports, aquaculture ponds, and salt pans, in Guangdong Province from Landsat 8 and Sentinel 2 images. The algorithms of a support vector machine, random forest, decision tree, and rule-based algorithm were performed. classification results were compared with statistical measures to assess the performance of different algorithms. The results indicated that all of the algorithms could obtain classification results with high accuracy, whereas the object-oriented algorithm showed less efficiency than other algorithms in classifying ports with complicated features. High-resolution data were not always superior to lower-resolution data in the reclamation classification. Generally speaking, applying the rule-based object-oriented algorithm in Sentinel 2A MSI images is relatively efficient at detecting the reclamation use in coastal Guangdong considering its actual situation. The mapping of reclamations in the whole of coastal Guangdong shows that they present obvious agglomeration characteristics in the space. The aquaculture ponds are mainly distributed in the coastal zones of western Guangdong and eastern Guangdong, with the largest area of 77,963 ha. The other types of ports are mainly distributed in the coastal zones of the Pearl River Delta, with an area of 8146 ha, while salt pans are mainly distributed in the coastal zones of Jiangmen, Zhuhai, and Zhongshan, with a total area of 407

关键词： reclamation detection classification algorithms object-oriented support vector machine random forest decision tree

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Basic Tenets of classification algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

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Journal of Data Analysis and Information Processing 2020年第4期8卷 341-357页

作者： Ernest Yeboah Boateng Joseph Otoo Daniel A. Abaye Department of Basic Sciences School of Basic and Biomedical Sciences University of Health and Allied Sciences Ho Ghana Department of Statistics and Actuarial Science University of Ghana Accra Ghana

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

关键词： classification algorithms Non-Parametric K-Nearest-Neighbor Neural Networks Random Forest Support Vector Machines

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A Comparative Study of classification algorithms for Early Detection of Diabetes

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Procedia Computer Science 2023年 220卷 868-873页

作者： César Carpinteiro João Lopes António Abelha Manuel Filipe Santos ALGORITMI/LASI Research Center University of Minho Portugal

Diabetes is a chronic disease that affects millions of people worldwide. Early detection of diabetes is crucial for preventing or delaying the onset of its associated complications. In this study, in collaboration with Unidade Local de Saúde do Alto Minho (ULSAM), we conducted a comprehensive comparison of various classification algorithms for the early detection of diabetes. We collected and pre-processed a dataset of patient records, containing personal information, associated medical problems and drugs. The dataset was divided into training and testing sets and used to train and evaluate several popular classification algorithms. The results of our study revealed that the Multilayer Perception (MLP), Gradient Boost Machine (GBM) and Random Forest (RF) algorithms had the highest overall performance, closely followed by Support Vector Machines. These findings demonstrate the potential of these algorithms for use in the early detection of diabetes and suggest that further research is needed to refine and optimize these models for clinical use.

关键词： Machine Learning classification algorithms Diabetes

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A Short Review of classification algorithms Accuracy for Data Prediction in Data Mining Applications

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Journal of Data Analysis and Information Processing 2021年第3期9卷 162-174页

作者： Ibrahim Ba’abbad Thamer Althubiti Abdulmohsen Alharbi Khalid Alfarsi Saim Rasheed Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah KSA

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances withi

关键词： Data Prediction Techniques Accuracy classification algorithms Data Mining Applications

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

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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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