<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

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

Department of Computer Science and Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

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

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 110 - 127

Eingereicht: 22. Juni 2023

Akzeptiert: 20. Okt. 2023

DOI: https://doi.org/10.2478/cait-2023-0039

Schlüsselwörter
Convex Least Angle Regression based Least Absolute Shrinkage and Selection Operator (CLAR-LASSO), Crunch base dataset, Startup, Survival rate, Swish activation function based Long Short Term Memory (LSTM)

© 2023 Ramakrishna Allu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 110 - 127

Eingereicht: 22. Juni 2023

Akzeptiert: 20. Okt. 2023

DOI: https://doi.org/10.2478/cait-2023-0039

SchlüsselwörterConvex Least Angle Regression based Least Absolute Shrinkage and Selection Operator (CLAR-LASSO), Crunch base dataset, Startup, Survival rate, Swish activation function based Long Short Term Memory (LSTM)

© 2023 Ramakrishna Allu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Convex Least Angle Regression based Least Absolute Shrinkage and Selection Operator (CLAR-LASSO), Crunch base dataset, Startup, Survival rate, Swish activation function based Long Short Term Memory (LSTM)