Survival analysis as semi-supervised multi-target regression for time-to-employment prediction using oblique predictive clustering trees.

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. • Estimation of time-to-employment of job seekers through structured output prediction. • The approach is based on Oblique Predictive Clustering Trees (SPYCTs). • Interpretation of the model predictions through the SHAP method. • Validation of the model performed on more than 1000 samples of job seekers data. • The model supports job seekers in finding optimal career solutions. [ABSTRACT FROM AUTHOR]