## 2 Methodology
### Classification Methods Used for Product-Lifespan Prediction  
Decision-making involves several approaches, including decision-tree classifiers [58]. Making a decision based on the structure of a decision tree allows complex decisions to be broken into a few small ones to deeply understand a problem. Decision trees are pervasive in a variety of real-world applications, including and not limited to medicinal research [59], biology, credit risk assessment, financial-market modeling, electrical engineering, quality control, biology, chemistry and so on. The evolution of web applications and social media resulted new areas of decision support and data analytics focused on user interaction and online behaviors. Decision trees are used for e-commerce, social media, online games, player segmentation, and other areas. Among other areas, applications include decision-tree usage for the future adoption of e-commerce-service predictions [60]. In social media, decision trees are used, for example, to predict the distance between users with Twitter activity data [61] and Twitter message classification with the use of the Classification and Regression Tree (CART) algorithm [62]. This wide area of applications includes online games with a focus on player-segmentation strategies based on self-recognition and game behaviors in the online game world to improve player satisfaction [63]. Integrated data-mining techniques such as association rule discovery, decision trees, and self-organizing map neural networks within the Kano model are used for customer-preference analysis in massively multiplayer online role-playing games [64]. Predicting aspects of playing behavior with the use of supervised learning algorithms is trained on large-scale player-behavior data. Decision-tree learning induces well-performing and informative solutions [65]. Rule databases can be used in a form of rule reasoner in online games for the detection of cheating activities [66], while a case-based reasoning approach can be applied for the purpose of training our system to learn and predict player strategies [67]. Educational games can be improved with decision trees used for the identification of factors affecting user behavior and knowledge acquisition within educational online games [68]. In other applications, decision trees are used for Internet game addiction in adolescents [69] and game-traffic analysis at the transport layer [70]. Clusterization techniques are used for player-behavior segmentation in computer games with the use of K-means and simplex volume maximization clustering [71], and user segmentation is used for retention management in online social games [72]. Integrated data-mining and experiential-marketing techniques can be used to segment online-game customers [73]. Owing to their structure, trees are easy to interpret, and hence result in better insights to problems. Nodes in decision-tree ramify from root nodes, and each node represents a condition related to a single input variable (feature), each branch represents a condition outcome, and each leaf node represents the class label. In this study, we applied CART [74], which is a binary tree. The method is to generate binary-tree-utilized binary-recursive partitioning that divides the dataset into two subsets, as per the minimization of a heterogeneity criterion computed on the resulting subsets. Each division made is based on a single variable, and some variables may not be used at all, while others may be used several times. Each subset is then further split based on independent rules. Let's take into account decision tree \(T\), with one of its leaves \(t\). \(T\) is a mapping that assigns a leaf \(t\) to each sample (X\({}^{1}_{i}\),..., X\({}^{p}_{i}\)), where i is an index for the samples. \(T\) can be viewed as a mapping to assign a value \(\widehat{Y_{i}}=\text{T}\) (X\({}^{1}_{i}\),..., X\({}^{p}_{i}\)) to each sample. Let \(p(j|t)\) be the proportion of a class \(j\) in a leaf \(t\). The Gini index and entropy are the two most popular heterogeneity criteria.