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Multiclass classification is one of the most fundamental tasks in data mining. However, traditional data mining methods rely on the model assumption, they generally can suffer from the overfitting problem on high dimension and low sample size (HDLSS) data. Trying to address multiclass classification problems on HDLSS data from another perspective, we utilize Genetic Programming (GP), an intrinsic evolutionary classification algorithm that can implement feature construction automatically without model assumption. This article develops an ensemble-based genetic programming classification framework, the Sigmoid-based Ensemble Gene Expression Programming (SE-GEP). To relieve the problem of output conflict in GP-based multiclass classifiers, the proposed method employs a flexible probability representation with continuous relaxation to better integrate the output of all the binary classifiers, an effective …