TY - GEN
T1 - Evaluation of machine learning classification algorithms & missing data imputation techniques
AU - Nwulu, Nnamdi I.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/30
Y1 - 2017/10/30
N2 - In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset which is publicly available, we investigate the use of four methods to impute or approximate missing values. The four imputed datasets are used to train MLP, SVM and VP classifiers to associate the congress members' responses to their political party affiliation and we compare the results from the three classifiers. The aim is to design a practical system or model to be able to predict another person's political affiliations based on their responses to similar questions. The obtained experimental results suggest that machine learning classifiers can be used to accurately predict an individual's political leaning.
AB - In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset which is publicly available, we investigate the use of four methods to impute or approximate missing values. The four imputed datasets are used to train MLP, SVM and VP classifiers to associate the congress members' responses to their political party affiliation and we compare the results from the three classifiers. The aim is to design a practical system or model to be able to predict another person's political affiliations based on their responses to similar questions. The obtained experimental results suggest that machine learning classifiers can be used to accurately predict an individual's political leaning.
KW - Missing data imputation
KW - Multi layer perceptron
KW - Support vector machines
KW - Voted perceptron
UR - http://www.scopus.com/inward/record.url?scp=85039922725&partnerID=8YFLogxK
U2 - 10.1109/IDAP.2017.8090315
DO - 10.1109/IDAP.2017.8090315
M3 - Conference contribution
AN - SCOPUS:85039922725
T3 - IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
BT - IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017
Y2 - 16 September 2017 through 17 September 2017
ER -