**九十七學年度下學期**** ****類神經網路**** ****研究計畫書**

**一、研究計畫中英文摘要：**請就本計畫要點作一概述，並依本計畫性質自訂關鍵詞。（五百字以內）

ABSTRACT

This proposal presents an intelligent inference
system to predict the propensity of customers, where artificial intelligence
(AI) techniques such as back propagation neural network (BPN) will be combined
to make good predictions of the target variables.

CRM (customer relationship management) is an information industry term for
methodologies, software, and usually Internet capabilities that help an
enterprise manage customer relationships in an organized way. For example, an
enterprise might build a database about its customers that described
relationships in sufficient detail so that management, salespeople, people
providing service, and perhaps the customer directly could access information,
match customer needs with product plans and offerings, remind customers of
service requirements, know what other products a customer had purchased, and so
forth. We focus on three properties: churn, appetency ,and up-selling.

In the first phase, we use several kinds of
sampling methods to analyze the value of input nodes and choose better
features. In the second phase, we try to find other potential features.
Finally, we adopt all proper features to implement our BPNs. After training,
the system performance was tested on test data sets provided by the French
Telecom company

Keywords：

Customer Relationship
Management (CRM), churn, appetency, up-selling, Back Propagation Network (BPN)

**二、研究計畫內容：**

（一）研究計畫之背景及目的。請詳述本研究計畫之背景、目的、重要性及國內外有關本計畫之研究情況、重要參考文獻之評述等。

（二）研究方法、進行步驟及執行進度。請列述：1.本計畫採用之研究方法與原因。2.預計可能遭遇之困難及解決途徑。

（三）預期完成之工作項目及成果。請列述：1.預期完成之工作項目。

（一）

Background

Customer Relationship Management (CRM) is a key element
of modern marketing strategies. CRM consists of the processes a company uses to
track and organize its contacts with its current and prospective customers. CRM software is used to support
these processes; information about customers and customer interactions can be
entered, stored and accessed by employees in different company departments. The KDD Cup 2009 offers the opportunity to work on large
marketing databases from the French Telecom company Orange to predict the
propensity of customers to switch provider (churn), buy new products or
services (appetency), or buy upgrades or add-ons proposed to them to make the
sale more profitable (up-selling).

Objective

In a CRM system, to build
knowledge on customer is to produce scores. A score (the output of a model) is
an evaluation for all instances of a target variable to explain (i.e. churn,
appetency or up-selling).Tools which produce scores allow to project, on a
given population, quantifiable information. The score is computed using input
variables which describe instances. Scores are then used by the information
system (IS), for example, to personalize the customer relationship. The rapid
and robust detection of the variables that have most contributed to the output
prediction can be a key factor in a marketing application. The task is to estimate the churn, appetency and
up-selling probability of customers, hence there are three target values to be
predicted. A large number of variables (15,000) are made available for
prediction.

（二） 1.本計畫採用之研究方法與原因。

The method of
this proposal is Back Propagation Network (BPN), which is the most
representative of the artificial neural networks. It is a kind of supervised
learning network and very powerful in terms of assessment and prediction. Many
practical applications are conducted to demonstrate the detection potential of
the BPN. It has the learning procedure and the recalling procedure.

BPN algorithm applies the basic principle of the gradient steepest descent
method to minimize the error function. It compares the outputs of the
processing units in the output layer with desired outputs to adjust the
connecting weights. The weights
between two neurons in two adjacent layers are adjusted through an iterative
training process while training samples are presented to the network. A closely
approximation of the transformation function which is compared the outputs of
the processing units in the output layer with desired outputs can be acquired.

There are
seven steps in BPN algorithm:

Step 1: Set network parameters.

Step2: Randomly generate the initial weight matrix and bias matrix for
input and hidden layers and weight matrix and bias matrix for hidden and output
layers.

Step 3: Input the training patterns X and desired output T.

Step 4: Compute the inferred output Y.

(1) Compute the
outputs of hidden layer H,

(2)
Compute the inferred output Y,

Step5: Compute δ.

(1) Compute δ of output layer.

(2) Compute δ of hidden layer.

Step 6: Adjust the weight matrix and the bias matrix ∆θ .

(1) Compute the weight
matrix of output layer and bias matrix of output layer, where η is the learning
rate.

(2) Compute the weight matrix of hidden layer and
bias matrix of hidden layer, where η is the learning rate.

Step 7: Update the weight matrix and the adjusted bias matrix.

(1) Update the weight matrix
of output layer and bias matrix of output layer,

(2) Update
the weight matrix of hidden layer and bias matrix of hidden layer,

Step 8: Repeat steps 3 to 7 until convergence *E *or the number of training iterations
exceeds the predefined threshold.

After having been trained, the network can be used to classify target data. The target data are then fed into the network, and the output with highest value will be
taken as the prediction.

The network construction is as below：

**Data selection**

There are a large number of variables (15,000) is made
available for prediction.

The first 14,740 variables are numerical and the last 260
are categorical, so data selection plays an important role in prediction. It is
defined as the process of determining the appropriate data type and source, as
well as suitable instruments to collect data. The process of selecting suitable
data for a research project can impact data integrity. A variety of sampling
procedures are available to reduce the likelihood of drawing a biased sample, and
some of them are listed below:

1. Simple random sampling

2. Stratified sampling

3. Cluster sampling

4. Systematic sampling

These methods of sampling try to ensure the
representativeness from the entire population by incorporating an element of
randomness to the selection procedure, and thus a greater ability to generalize
findings to the targeted population.

**Data transformation**

A
normalization process of the input data is necessary. It encodes the data to
fill into input and output layers of BPN model. We have designed proper input
and output nodes to utilize the architecture of NNs accompanying with the
characteristics of data.

2.預計可能遭遇之困難及解決途徑。

There are a large number of
variables (15,000) is made available for prediction. In
all of them, which variables should be extracted is a main problem. At the same time, how can we find all useful variables and abandon noise
variables? We probably spend a lot of time on data selection,
in which we choose Cluster sampling as the main method to get a small set of
variables that is enough to stand for customer properties. However, if we can’t
come to a good conclusion through this way, second choice is Simple random
sampling.

（三）預期完成之工作項目。

1. Data selection

2. Data transformation

3. The result of Back
Propagation Network by matlab