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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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Correlates of Hepatitis B Virus Infection among Antenatal Clinic Attendees of Volta Regional Hospital, Ho, Volta Region, Ghana

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Open Journal of Applied Sciences 2019年第7期9卷 573-586页

作者： joseph otoo Paschal Kofi Dah Ernest Yeboah Boateng Daniel A. Abaye Department of Statistics and Actuarial Science University of Ghana Accra Ghana Supercare Specialist Hospital Ho Ghana School of Basic and Biomedical Sciences University of Health and Allied Sciences Ho Ghana

Hepatitis B virus (HBV) infection remains a global health problem. With about 380 million chronic carriers of the HBV virus, there are over two million global deaths annually. Ghana is among the high endemic countries in Africa, with HBV prevalence ranging from 4.8% to 12.3% in the general population, 10.8% to 12.7% in blood donors and about 10.6% in antenatal clinic (ANC) attendees. The main objectives of this study were to test how socioeconomic factors, risky behaviors, knowledge and awareness of HBV infection correlate with actual HBV status among antenatal clinic attendees and to determine the predictors of HBV testing among ANC attendees. The study employed random sampling technique to sample 500 pregnant women, at mothers’ clinic of Volta Regional Hospital, Ho, Ghana. A structured questionnaire was used to collect information on socio-demographic characteristics, Hepatitis B status, possible risk factors, awareness and knowledge levels of HBV infection. Cross tabulation and the chi-square (χ2) statistic were used to determine statistical independence or association of study variables. Kruskal-Wallis test was applied to test for the differences in HBV knowledge scores across HBV status and levels of HBV awareness;and the binomial regression model was used to determine the predictors of HBV testing among ANC attendees. It is evident that age, religion, ethnicity, educational level, blood transfusion, number of blood transfusions, gravidity, parity, awareness of HBV and monthly income were associated with HBV status. Results of the Binomial Logistic Regression model indicate that Age (p = 0.03), Education level (p = 0.04), Religion (p = 0.04), Ethnicity (p = 0.00) and Blood transfusion (p = 0.04) were significant (p 0.05) predictors of HBV testing. Knowledge of HBV status enables patients to seek early treatment, facilitates referral for social support and counseling. We recommend that the Ministry of Health should carry effective education on HBV and its preven

关键词： Binomial Logistic Regression Hepatitis B Virus Infection Antenatal Clinic Attendees

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Morphological Characterization of Some Water Yam （Dioscorea alata L.） Germplasm in Ghana

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Journal of Agricultural Science and Technology(B) 2014年第7期4卷 518-532页

作者： Michael Anokye joseph Paddy Tettch Emmanuel otoo Department of Crop Science University of Cape Coast Ghana Crops Research Institute Kumasi 3785 Ghana

The Asiatic Dioscorea alata yam species is an important food crop, especially in Cote d＇lvoire. It has high yield and stores longer than Dioscorea cayenensis/rotundata yam and therefore fills the hunger gap created when other yam types arc not available. However, very little research has been done on it. Several cultivars are susceptible to pests and diseases and lack the aesthetic values of smooth skin and elegant tuber shape ofDioscorea rotundata that appeal to consumers in the market. This study therefore sought to establish the genetic variability and relationships among 35 accessions of Ghanaian Dioscorea alata together with 14 accessions introduced from International Institute of Tropical Agriculture in Nigeria. Morphological variation was studied to establish genetic relationship among 49 accessions assembled as Dioscorea alata in Ghana. Principal component analysis （PCA） indicated that the first two principal components accounted for 25.27% of total variation from accessions with PCA1 and PCA2, contributing 13.92% and 11.35%, respectively. Cluster analysis performed clearly separated the 49 accessions into three main groupings using unweighted neighbour-joining method. Morphological descriptors were able to group the accessions into distinct clusters independent of place of collection.

关键词： Dioseorea alata germplasm characterization morphological Ghana.

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