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.