[作業] 十個英文句子[樣板]

sentence (1).
Decision tree learning is one of the most widely used and practical methods for inductive inference.

sentence (2).
To construct a decision tree, we have to select appropriate attributes as the tree nodes.

sentence (3).
Many methods are available for attribute selection, such as the entropy based methods[3,4], Bayesian networks [5], gini index methods [6,7], etc.

sentence (4.1).
Rough set theory, proposed by Poland mathematician Pawlak in 1982, is a new mathematic tool to deal with vagueness and uncertainty [9].

sentence (4.2).
Pfeifer and Carraway (2000) proposed Markov Chain Models for modeling customer relationships.

sentence (4.3)
Colombo and Jiang (1999) developed a stochastic Recency Frequency Monetary model to rank customers in terms of their expected contribution.

sentence (4.4)
More detailed discussion about the process of rough set theory can refer to Walczak and Massart (1999).

sentence (5.1)
However, the proposed approach also has its limitations. It can do well only in accurate classification where objects are strictly classified according to equivalence classes, hence the induced classifiers lack the ability to tolerate possible noises in real world data sets.

sentence (5.2)
However, the long-term value does not fit for the industry having stiff competitions and rapid changes of market environments. Especially, it is not easy to evaluate the LTV of customers in the wireless communication industyr, which are very sensitive to the external environments and the customer defections. Hence, this study focuses on the short-term value of customers of a wireless communication industry.

sentence (6.1)
Customer value is classified into three categories: current value, potential value, and customer loyalty.

sentence (6.2)
customers are segmented according to three types of customer value.

sentence (6.3)
Verhoef and Donkers (2001) used two dimensions, current value and potential value, to segment the customers of an insurance company.

sentence (6.4)
We use three dimension, current value, potential value, and customer loyalty, to consider the customer definition in this study.

sentence(7.1)
We describe three popular models used in building credit scoring models. The first model is logistic regression, which is mostly used for classification problems in the area of statistics. The second model is ANN, which is known for its excellent ability of learning non-linear relationships in a system. The third model is rough sets, which is one kind of induction based algorithms, and has been widely used in classification problems since 1990s.

sentence(8.1)
Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression.

sentence(8.2)
In this section, GP is compared to MLP, classification and regression tree (CART), C4.5, Rough sets, and logistic regression (LR) using two-real world data sets.

The first data set includes Australian credit scoring data with 307 examples of credit worthy customers and 383 examples for credit unworthy customers. It contains 14 attributes, where six are continuous attributes and eight are categorical attributes.