Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
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The process of buying stock, the major indicator is earning per share (EPS). It is the earning return on original investment; it represents the profit ability of common stock, and the final result of company performance. Therefore, in this study integrates financial-statement related indicators to predict the future EPS. Base on literatures, the relationship of EPS and related attributes is nonlinear, and the nonlinear model can predict well in EPS, so we propose an integrated Adaptive Network-Based Fuzzy Inference System (ANFIS). It combines with the decision tree which is the pre-process for enhancing predicting ability, and there are three stages in study (1) use feature selection to reduce attributes, and the attributes are discretized by decision tree, then encoding the dicretization value (2) take fuzzy inference system (FIS) to fuzzify the encoding value, and use adaptive network to tune optimal parameters. (3) employ an integrated ANFIS model to predict EPS. We collect ninequarter EPS data for predicting, and then the proposed method surpasses in accuracy these conventional data mining models.