Abstract
Due to great changes in the global economy, corporate financial distress prediction is playing an increasingly vital role in this highly competitive environment. However, one key factor of financial distress is poor management, and business operating efficiency is a suitable reflection of corporate management. The multi-agent architecture introduced herein, namely the ROER model, consists of three main parts for corporate operation efficiency forecasting: (1) data envelopment analysis with random projection, (2) online sequential-extreme learning model (OS-ELM), and (3) rough set theory (RST). The ROER model is grounded on ensemble learning/multi-agent learning, of which the core elements are preciseness and diversity. We achieve the goal of generating a diverse model through two ensemble strategies: (1) modifying the inherent parameters of RP, and (2) adjusting the activation type, block size, and parameters of OS-ELM. The nature of the multi-agent architecture is opaque, making it complicated for users to comprehend as well as impeding its empirical application. To handle the black-box problem, this study implements RST to extract the decision knowledge from the multi-agent model and to represent knowledge in an ‘if-then’ format, which is easier to understand. In the modern economy, the essential component of any value-creating business activity has changed from physical assets to intangible assets. Thus, we examine intangible assets, with empirical results revealing that the proposed ROER model is a promising alterative for the performance forecasting task.
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References
Achlioptas D (2001) Database-friendly random projections. In: Symposium on principles of database systems (PODS), pp 274–281
Al-Refaie A, Fouad RH, Li MH, Shurrab M (2014) Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simul Model Pract Theory 41:59–72
Antal B, Hajdu A (2014) An ensemble-based system for automatic screening of diabetic retinopathy. Knowl-Based Syst 60:20–27
Brigham E, Maninila H (2001) Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the conference on knowledge discovery and data mining, vol 16. pp 245–250
Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52:489–509
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444
Chen S, Wang W, Zuylen H (2009) Construct support vector machine ensemble to detect traffic incident. Expert Syst Appl 36:10976–10986
Chen Y, Miao D, Wang R (2010) A rough set approach to feature selection based on ant colony optimization. Pattern Recogn Lett 31:226–233
Deogun J, Choubey S, Raghavan V, Sever H (1998) Feature selection and effective classifiers. J Am Soc Inf Sci 5:403–414
Dasgupta S, Gupta A (1999) An elementary proof of the Johnson-Lindenstrauss lemma. Technica lreprot, International Computer Science Institute, Berkeley, CA
Dasgupta S (2000) Experiments with random projection. In: Proceedings of the conference on uncertainty in artificial intelligence
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180:2044–2064
Gleason KI, Klock M (2006) Intangible capital in the pharmaceutical and chemical industry. Q Rev Econ Financ 46:300–314
Guthrie J, Ricceri F, Dumay J (2012) Reflections and projections: a decade of intellectual capital accounting research. Br Acc Rev 44:68–82
Golub GH, Loan CF (1983) Matrix Computations. North Oxford Academic, Oxford
Gonzalez-Abril L, Cuberos FJ, Velasco F, Ortega JA (2009) Ameva: an autonomous discretization algorithm. Expert Syst Appl 36:5327–5332
He J, Ding L, Jiang L, Li Z, Hu Q (2014) Intrinsic dimensionality estimation based on manifoldassumption. J Vis Commun Image R 25:740–747
Hecht-Nielsen R (1994) Context vectors: general purpose approximate meaning representations self-organized from raw data. In: Zurada JM, Marks RJ II, Robinson CJ (eds) Computational intelligence: imitating life. IEEE Press, New York
Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks, Budapest, pp 25–29
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892
Huang GB, Ding XJ, Zhou HM (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74:155–163
Huang GB, Zhou HM, Ding XJ, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B 42:513–529
Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48
Hor C, Crossley PA, Millar DL (2007) Application of genetic algorithm and rough set theory for knowledge extraction. IEEE Lausanne Power Tech 1117–1122
Jeong KH, Principe JC (2008) Enhancing the correntropy MACE filter with random projections. Neurocomputing 72:102–111
Jensen R, Shen Q (2004) Semantics-preserve dimensionality reduction: Rough and fuzzy-rough-based approaches. IEEE Trans Knowl Data Eng 16:1457–1471
Lee ZY, Pai CC (2011) Operation analysis and performance assessment for TFT-LCD manufacturers using improved DEA. Expert Syst Appl 38:4014–4024
Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423
Lin TH (2009) A cross model study of corporate financial distress prediction in Taiwan: multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing 72:3507–3516
Lin SJ, Chang C, Hsu MF (2013) Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction. Knowl-Based Syst 39:214–223
Lin SJ, Hsu MF (2014) Enhanced risk management by an emerging multi-agent architecture. Connect Sci 26:245–259
Liu Y, Yu X, Huang JX, An A (2011) Combining integrated sampling with SVM ensembles forlearning from imbalanced datasets. Inform Process Manag 47:617–631
Liu X, Wang L, Huang GB, Zhang J, Yin J (2015) Multiple kernel extreme learning machine. Neurocomputing 149:253–264
Mirza B, Lin Z, Toh KA (2013) Weighted online sequential extreme learning machine for class imbalance learning. Neural Process Lett 38:465–486
Park J, Sandberg IW (1991) Universal approximation using radial basis function networks. Neural Comput 3:246–257
Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356
Pietruskiewicz W (2008) Dynamic systems and nonlinear Kalman filtering applied in classification. In: Proceedings of \(7^{\rm th}\) IEEE international conference on cybernetic intelligent systems, 263–268
Ruta D, Gabrys B (2006) Classifier selection for majority voting. Inform Fusion 6:63–81
Samoilenko S, Osei-Bryson KM (2013) Using data envelopment analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system. Omega 41:131–142
Tsai CF, Lu YH, Yen C (2012) Determinants of intangible assets value: the data mining approach. Knowl-Based Syst 31:67–77
Wang XZ, Shao QY, Miao Q, Zhai JH (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9
Wroblewski J (1995) Finding minimal reducts using Genetic Algorithm. In: 2nd Annual join conference on information sciences, 186–189
Xu X, Wang Y (2009) Financial failure prediction using efficiency as a predictor. Expert Syst Appl 36:366–373
Yang CH, Motohashi K, Chen JR (2009) Are new technology-based firms located on science parks really more innovative?: Evidence from Taiwan. Res Policy 38:77–85
Ye Y, Squartini S, Piazza F (2013) Online sequential extreme learning machine in nonstationary environments. Neurocomputing 116:94–101
Yin JC, Zou ZJ, Xu F, Wang NN (2014) Online ship roll motion prediction based on grey sequential extreme learning machine. Neurocomputing 129(10):168–174
Yu W, Zhuang F, He Q, Shi Z (2015) Learning deep representations via extreme learning machines. Neurocomputing 149:308–315
Zhai JH, Xu HY, Wang XZ (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16:1493–1502
Zhai J, Xu H, Li Y (2013) Fusion of extreme learning machine with fuzzy integral. Int J Uncertain Fuzz 21:23–34
Zhou Z, Zhao L, Lui S, Ma C (2012) A generalized fuzzy DEA/AR performance assessment model. Math Comput Model 55:2117–2128
Zong W, Huang GB (2014) Learning to rank with extreme learning machine. Neural Process Lett 39:155–166
Acknowledgments
We thank Editor-in-Chief Professor Dr. Verleysen and Professor Dr. Hassoun and anonymous reviewers for helpful comment and suggestions that improved the paper. The author would like to thanks Professor Hsu Pao and Ministry of Science and Technology of the Republic of China, Taiwan for financially supporting this work under Contract No. 103-2410-H-034 -029.
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Lin, SJ., Chen, TF. Multi-agent Architecture for Corporate Operating Performance Assessment. Neural Process Lett 43, 115–132 (2016). https://doi.org/10.1007/s11063-014-9405-2
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DOI: https://doi.org/10.1007/s11063-014-9405-2