Determination of essential features for predicting start-up success: an empirical approach using machine learning

Pasayat, Ajit Kumar; Mitra, Adway; Bhowmick, Bhaskar

doi:10.1080/09537325.2022.2116569

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…These aforementioned features support the SAFLSTM to train effectively, therefore historical sequence data is completely used in the classification. The process of SAFLSTM is formulated in the following equations: (5) 𝑓 𝑡 = Swish (𝑊 f . [ℎ 𝑡−1 , 𝑥 𝑡 ] + 𝑏 f ), (6)…”

Section: Classification Using Saflstmmentioning

confidence: 99%

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Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

Allu,

Padmanabhuni

2023

Cybernetics and Information Technologies

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A startup is a recently established business venture led by entrepreneurs, to create and offer new products or services. The discovery of promising startups is a challenging task for creditors, policymakers, and investors. Therefore, the startup survival rate prediction is required to be developed for the success/failure of startup companies. In this paper, the feature selection using the Convex Least Angle Regression Least Absolute Shrinkage and Selection Operator (CLAR-LASSO) is proposed to improve the classification of startup survival rate prediction. The Swish Activation Function based Long Short-Term Memory (SAFLSTM) is developed for classifying the survival rate of startups. Further, the Local Interpretable Model-agnostic Explanations (LIME) model interprets the predicted classification to the user. Existing research such as Hyper Parameter Tuning (HPT)-Logistic regression, HPT-Support Vector Machine (SVM), HPT-XGBoost, and SAFLSTM are used to compare the CLAR-LASSO. The accuracy of the CLAR-LASSO is 95.67% which is high when compared to the HPT-Logistic regression, HPT-SVM, HPT-XGBoost, and SAFLSTM.

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Section: Classification Using Saflstmmentioning

confidence: 99%

“…Worldwide, the SME's success rate is only around 40% [3,4]. There are many startups developing effective services and products such as Zomato, Facebook, WhatsApp, Instagram, Snowflake, Paytm, and Uber [5]. The discovery of hopeful startups is a challenging task for creditors, policymakers, and investors.…”

Section: Introductionmentioning

confidence: 99%

Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

Allu,

Padmanabhuni

2023

Cybernetics and Information Technologies

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“…Over the years, researchers have explored various approaches to predict startup success, employing statistical analyses and machine learning techniques (Allu & Padmanabhuni, 2020; Pasayat et al, 2022). By analyzing historical data and extracting patterns, predictive models have been developed to aid decision making in the dynamic and uncertain world of startups (Brown & Rocha, 2020; Khayrutdinov et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Machine learning models for early‐stage investment decision making in startups

Shi,

Eremina,

Long

2023

Manage Decis Econ

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This study demonstrates the efficacy of machine learning techniques for evidence‐based evaluation of early‐stage ventures. Leveraging real‐world data on 24,965 startups across diverse sectors and countries sourced from Crunchbase, we develop predictive models using algorithms including random forest, XGBoost, and support vector machines. Rigorous training and testing on a 70–30 split of the data reveal that the algorithms can effectively classify startups as successful or not, achieving over 90% accuracy. Random forest emerges as the top performer, followed closely by XGBoost. This research demonstrates the immense potential of machine learning techniques in forecasting startup success to inform management practice.

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Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets

Behera

Pasayat

Behera

et al. 2023

Engineering Applications of Artificial Intelligence

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Determination of essential features for predicting start-up success: an empirical approach using machine learning

Cited by 3 publications

References 29 publications

Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

Machine learning models for early‐stage investment decision making in startups

Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets

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