International Journal of Management and Applied Science (IJMAS)
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Statistics report
Mar. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 59
Paper Published : 3874
No. of Authors : 7966
  Journal Paper




Paper Title :
Using Heuristic Decision Classifier to Predict The Operational Performance of Enterprises in Taiwan

Author :Chen Tung Chen, Wei Zhan Hung

Article Citation :Chen Tung Chen ,Wei Zhan Hung , (2018 ) " Using Heuristic Decision Classifier to Predict The Operational Performance of Enterprises in Taiwan " , International Journal of Management and Applied Science (IJMAS) , pp. 64-67, Volume-4,Issue-9

Abstract : Investment analysis is an important issue for everyone in the society. However, there are many factors will influence the operational performance of enterprises. Under this situation, it is difficult to make a decision to select the good goals to invest. This research applies meta-heuristic decision classifier (MHDC) to predict the operational performance of each enterprise by using the data which is collected from open information observatory station in Taiwan. Based on the experiment results, the accuracy of test data by using MHDC is higher than other machine learning methods such as SVM classifier, KNN classifier, Ada Boost classifier and BPNN classifier. Finally, some conclusions and future research directions can be discussed as ending. Keywords- Investment Analysis, Operational Performance, Decision Classifier, Machine Learning.

Type : Research paper


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