9. Evaluation Analysis

Evaluation is one of the key steps in big data analytics, which determines the merit of data analysis towards the experimental objectives. It usually relates a trade-off comparison of multiple criteria which may conflict each other or complex interpretations of the problems in nature. This chapter provides several of evaluation models of the recent studies on data science. Section 9.1 reviews three evaluation formations for the known methodologies. Section 9.1.1 describes a decision-making support for the evaluation of clustering algorithms based on multiple criteria decision making (MCDM) [1]. Section 9.1.2 is about evaluation of classification algorithms using MCDM and rank correlation [2]. Section 9.1.3 discusses the public blockchain evaluation using entropy and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) [3]. Section 9.2 outlines two evaluation methods for Software. Section 9.2.1 is about a classifier evaluation for software defect prediction [4], while Sect. 9.2.2 is about an ensemble of software defect predictors by AHP-based evaluation method [5]. Section 9.3 describes four evaluation methods for sociology and economics. Section 9.3.1 is about a delivery efficiency and supplier performance evaluation in China’s E-retailing industry [6]. Section 9.3.2 is about the credit risk evaluation with Kernel-based affine subspace nearest points learning method [7]. Section 9.3.3 is a dynamic assessment method for urban eco-environmental quality evaluation [8], while Sect. 9.3.4 is an empirical study of classification algorithm evaluation for financial risk prediction [9].