Survival analysis as semi-supervised multi-target regression for time-to-employment prediction using oblique predictive clustering trees
Authors/Creators
- 1. Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
- 2. Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
- 3. Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Faculty of Information Studies in Novo mesto, Ljubljanska cesta 31b, 8000 Novo mesto, Slovenia
Description
Highlights
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Estimation of time-to-employment of job seekers through structured output prediction.
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The approach is based on Oblique Predictive Clustering Trees (SPYCTs).
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Interpretation of the model predictions through the SHAP method.
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Validation of the model performed on more than 1000 samples of job seekers data.
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The model supports job seekers in finding optimal career solutions.
Abstract
We address the problem of estimating the time-to-employment of a jobseeker using survival analysis and oblique predictive clustering tree. Unlike standard survival analysis, oblique predictive clustering tree can handle categorical and continuous data and is capable of modelling non-linear dependences. Treating the censored data as missing data opens the possibility to perform survival analysis by using structured output prediction in semi-supervised multi-target regression setting. The effectiveness of this approach is shown on a real dataset from Public Employment Services in Slovenia, comprising time-to-employment records with jobseekers’ personal and professional characteristics. The performances are compared with six state-of-the-art AI methods. To the best of our knowledge, this is the first example of using semi-supervised oblique predictive clustering tree for survival analysis.