Top

Published in:

2023 | OriginalPaper | Chapter

Impact of High Dimensionality Reduction in Financial Datasets of SMEs with Feature Pre-processing in Data Mining

Authors : R. Mahalingam, K. Jayanthi

Published in: Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

High Data Dimensionality Reduction (HDDR) removes the irrelevant features in a complex dataset and incorporates various techniques that could be used to foretell the research outcomes in a predictive model. The major objective of the paper is to analyse and survey the various models on the basis of HDDR and its feature pre-processing methods applied in financial dataset predictions. Numerous techniques of data mining and its strategies were discussed and assessed to ascertain the importance of augmenting the performance of the financial dataset with classifiers. The pre-processing techniques applied in various research works and their outcomes are highlighted. The HDDR methods used in financial prediction of Small Medium Enterprises (SMEs) are studied for existing frameworks and models by different authors. The paper encapsulates the gist of the models, frameworks and algorithms involved in effective elimination of irrelevant features and extraction of best features for best prediction of financial datasets.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Low-Noise Amplifier with Co-designed Microstrip Antenna for 60 GHz Wireless Communications

next chapter Deep Learning-Based Triphase Community Detection for Multimedia Data

Chhikara P, Jain N, Tekchandani R, Kumar N (2022) Data dimensionality reduction techniques for Industry 4.0: Research results, challenges, and future research directions. Soft Pract Experience 52(3):658–688

Ray P, Reddy SS, Banerjee T (2021) Various dimension reduction techniques for high dimensional data analysis: a review. Artif Intell Rev 54(5):3473–3515CrossRef

Thrun MC, Ultsch A (2021) Using projection-based clustering to find distance-and density-based clusters in high-dimensional data. J Classif 38(2):280–312MathSciNetCrossRefMATH

Ayesha S, Hanif MK, Talib R (2020) Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf Fusion 59:44–58. https://doi.org/10.1016/j.inffus.2020.01.005CrossRef

Xu X, Liang T, Zhu J, Zheng D, Sun T (2019) Review of classical dimensionality reduction and sample selection methods for large-scale data processing. Neurocomputing 328:5–15. https://doi.org/10.1016/j.neucom.2018.02.100CrossRef

Espadoto M, Martins RM, Kerren A, Hirata NS, Telea AC (2019) Toward a quantitative survey of dimension reduction techniques. IEEE Trans Visual Comput Graphics 27(3):2153–2173. https://doi.org/10.1109/TVCG.2019.2944182CrossRef

Chormunge S, Jena S (2018) Correlation based feature selection with clustering for high dimensional data. J Electr Syst Inf Technol 5(3):542–549. https://doi.org/10.1016/j.jesit.2017.06.004CrossRef

Solorio-Fernández S, Carrasco-Ochoa JA, Martínez-Trinidad JF (2020) A review of unsupervised feature selection methods. Artif Intell Rev 53(2):907–948. https://doi.org/10.1007/s10462-019-09682-yCrossRef

Nguyen BH, Xue B, Zhang M (2020) A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol Comput 54:100663. https://doi.org/10.1016/j.swevo.2020.100663CrossRef

10.

Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst Appl 92:507–520. https://doi.org/10.1016/j.eswa.2017.09.058CrossRef

11.

Thudumu S, Branch P, Jin J, Singh JJ (2020) A comprehensive survey of anomaly detection techniques for high dimensional big data. J Big Data 7(1):1–30. https://doi.org/10.1186/s40537-020-00320-xCrossRef

12.

Pes B (2020) Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains. Neural Comput Appl 32(10):5951–5973. https://doi.org/10.1007/s00521-019-04082-3CrossRef

13.

Liu Z, Lai Z, Ou W, Zhang K, Zheng R (2020) Structured optimal graph based sparse feature extraction for semi-supervised learning. Signal Process 170:107456. https://doi.org/10.1016/j.sigpro.2020.107456CrossRef

14.

Nkwabi J, Mboya L (2019) A review of factors affecting the growth of small and medium enterprises (SMEs) in Tanzania. Eur J Bus Manage 11(33):1–8. https://doi.org/10.7176/EJBM/11-33-01CrossRef

15.

Rao P, Kumar S, Madhavan V (2019) A study on factors driving the capital structure decisions of small and medium enterprises (SMEs) in India. IIMB Manage Rev 31(1):37–50. https://doi.org/10.1016/j.iimb.2018.08.010CrossRef

16.

Lin FJ, Ho CW (2019) The knowledge of entry mode decision for small and medium enterprises. J Innov Knowl 4(1):32–37. https://doi.org/10.1016/j.jik.2018.02.001CrossRef

17.

Nasution MI, Fahmi M, Prayogi MA (2020) The quality of small and medium enterprises performance using the structural equation model-part least square (SEM-PLS). J Phys: Conf Ser 1477(5):052052. IOP Publishing

18.

Papadopoulos T, Baltas KN, Balta ME (2020) The use of digital technologies by small and medium enterprises during COVID-19: implications for theory and practice. Int J Inf Manage 55:102192. https://doi.org/10.1016/j.ijinfomgt.2020.102192CrossRef

19.

Hanggraeni D, Ślusarczyk B, Sulung LAK, Subroto A (2019) The impact of internal, external and enterprise risk management on the performance of micro, small and medium enterprises. Sustainability 11(7):2172. https://doi.org/10.3390/su11072172CrossRef

20.

Abbas J, Zhang Q, Hussain I, Akram S, Afaq A, Shad MA (2020) Sustainable innovation in small medium enterprises: the impact of knowledge management on organizational innovation through a mediation analysis by using SEM approach. Sustainability 12(6):2407. https://doi.org/10.3390/su12062407CrossRef

21.

Malakauskas A, Lakštutienė A (2021) Financial distress prediction for small and medium enterprises using machine learning techniques. Eng Econ 32(1):4–14. https://doi.org/10.5755/j01.ee.32.1.27382CrossRef

22.

Ciampi F, Giannozzi A, Marzi G, Altman EI (2021) Rethinking SME default prediction: a systematic literature review and future perspectives. Scientometrics 126(3):2141–2188. https://doi.org/10.1007/s11192-020-03856-0CrossRef

23.

Kou G, Xu Y, Peng Y, Shen F, Chen Y, Chang K, Kou S (2021) Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decis Support Syst 140:113429. https://doi.org/10.1016/j.dss.2020.113429CrossRef

24.

Sun J, Fujita H, Zheng Y, Ai W (2021) Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods. Inf Sci 559:153–170. https://doi.org/10.1016/j.ins.2021.01.059MathSciNetCrossRef

25.

Sankhwar S, Gupta D, Ramya KC, Sheeba Rani S, Shankar K, Lakshmanaprabu SK (2020) Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction. Soft Comput 24(1):101–110. https://doi.org/10.1007/s00500-019-04323-6CrossRef

26.

Shang H, Lu D, Zhou Q (2021) Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules. Neural Comput Appl 33(9):3901–3909. https://doi.org/10.1007/s00521-020-05510-5CrossRef

27.

Uthayakumar J, Metawa N, Shankar K, Lakshmanaprabu SK (2020) Financial crisis prediction model using ant colony optimization. Int J Inf Manage 50:538–556. https://doi.org/10.1016/j.ijinfomgt.2018.12.001CrossRef

28.

Subasi A, Cankurt S (2019) Prediction of default payment of credit card clients using data mining techniques. In: 2019 International engineering conference (IEC), pp 115–120. IEEE. https://doi.org/10.1109/IEC47844.2019.8950597

29.

Laborda R, Olmo J (2021) Volatility spillover between economic sectors in financial crisis prediction: evidence spanning the great financial crisis and covid-19 pandemic. Res Int Bus Financ 57:101402. https://doi.org/10.1016/j.ribaf.2021.101402CrossRef

30.

Jabeur SB, Sadaaoui A, Sghaier A, Aloui R (2020) Machine learning models and cost-sensitive decision trees for bond rating prediction. J Oper Res Soc 71(8):1161–1179. https://doi.org/10.1080/01605682.2019.1581405CrossRef

31.

Kim S, Ku S, Chang W, Song JW (2020) Predicting the direction of US stock prices using effective transfer entropy and machine learning techniques. IEEE Access 8:111660–111682. https://doi.org/10.1109/ACCESS.2020.3002174CrossRef

32.

Cheng KC, Huang MJ, Fu CK, Wang KH, Wang HM, Lin LH (2021) Establishing a multiple-criteria decision-making model for stock investment decisions using data mining techniques. Sustainability 13(6):3100. https://doi.org/10.3390/su13063100CrossRef

33.

Jan CL (2021) Financial information asymmetry: using deep learning algorithms to predict financial distress. Symmetry 13(3):443. https://doi.org/10.3390/sym13030443CrossRef

34.

Moradi S, Rafiei FM (2019) A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks. Financ Innov 5(1):1–27. https://doi.org/10.1186/s40854-019-0121-9CrossRef

35.

Van Nguyen T, Zhou L, Chong AYL, Li B, Pu X (2020) Predicting customer demand for remanufactured products: a data-mining approach. Eur J Oper Res 281(3):543–558. https://doi.org/10.1016/j.ejor.2019.08.015CrossRef

36.

Jahangir H, Tayarani H, Baghali S, Ahmadian A, Elkamel A, Golkar MA, Castilla M (2019) A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks. IEEE Trans Industr Inf 16(4):2369–2381. https://doi.org/10.1109/TII.2019.2933009CrossRef

37.

Bai Y, Sun Z, Zeng B, Long J, Li L, de Oliveira JV, Li C (2019) A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. J Intell Manuf 30(5):2245–2256. https://doi.org/10.1007/s10845-017-1388-1CrossRef

38.

Huang H, Shi G, He H, Duan Y, Luo F (2019) Dimensionality reduction of hyperspectral imagery based on spatial–spectral manifold learning. IEEE Trans Cybern 50(6):2604–2616. https://doi.org/10.1109/TCYB.2019.2905793CrossRef

39.

Ding J, Condon A, Shah SP (2018) Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat Commun 9(1):1–13. https://doi.org/10.1038/s41467-018-04368-5CrossRef

40.

Aydadenta H, Adiwijaya A (2018) A clustering approach for feature selection in microarray data classification using random forest. J Inf Process Syst 14(5):1167–1175. https://doi.org/10.3745/JIPS.04.0087CrossRef

41.

Selvakumar B, Muneeswaran K (2019) Firefly algorithm-based feature selection for network intrusion detection. Comput Secur 81:148–155. https://doi.org/10.1016/j.cose.2018.11.005CrossRef

42.

Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27(5):2420–2432. https://doi.org/10.1109/TIP.2018.2804218MathSciNetCrossRefMATH

43.

Abdulhammed R, Musafer H, Alessa A, Faezipour M, Abuzneid A (2019) Features dimensionality reduction approaches for machine learning based network intrusion detection. Electronics 8(3):322. https://doi.org/10.3390/electronics8030322CrossRef

44.

Becht E, McInnes L, Healy J, Dutertre CA, Kwok IW, Ng LG, Ginhoux F, Newell EW (2019) Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 37(1):38–44. https://doi.org/10.1038/nbt.4314

45.

Salo F, Nassif AB, Essex A (2019) Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Comput Netw 148:164–175. https://doi.org/10.1016/j.comnet.2018.11.010CrossRef

46.

Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol 20(1):1–16. https://doi.org/10.1186/s13059-019-1861-6CrossRef

47.

Ali F, El-Sappagh S, Islam SR, Ali A, Attique M, Imran M, Kwak KS (2021) An intelligent healthcare monitoring framework using wearable sensors and social networking data. Futur Gener Comput Syst 114:23–43. https://doi.org/10.1016/j.future.2020.07.047CrossRef

48.

Elezaj O, Yayilgan SY, Abomhara M, Yeng P, Ahmed J (2019) Data-driven intrusion detection system for small and medium enterprises. In: 2019 IEEE 24th international workshop on computer aided modeling and design of communication links and networks (CAMAD), pp 1–7. IEEE. https://doi.org/10.1109/CAMAD.2019.8858166

49.

Stjepić AM, Pejić Bach M, Bosilj Vukšić V (2021) Exploring risks in the adoption of business intelligence in SMEs using the TOE framework. J Risk Financ Manage 14(2):58. https://doi.org/10.3390/jrfm14020058CrossRef

50.

Gao G, Wang H, Gao P (2021) Establishing a credit risk evaluation system for SMEs using the soft voting fusion model. Risks 9(11):202. https://doi.org/10.3390/risks9110202CrossRef

51.

Shakya S, Smys S (2021) Big data analytics for improved risk management and customer segregation in banking applications. J ISMAC 3(03):235–249. https://doi.org/10.36548/jismac.2021.3.005

52.

Suma V (2019) Towards sustainable industrialization using big data and internet of things. J ISMAC 1(01):24–37. https://doi.org/10.36548/jismac.2019.1.003

Title: Impact of High Dimensionality Reduction in Financial Datasets of SMEs with Feature Pre-processing in Data Mining
Authors: R. Mahalingam
K. Jayanthi
Publisher: Springer Nature Singapore
Book: Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems
Print ISBN: 978-981-19-7752-7

Electronic ISBN: 978-981-19-7753-4

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-19-7753-4_29

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"