Classification Accuracy Of Statistical Software Performance For Default Of Credit Card Clients Essay

The concepts in this assignment are mostly of comprehensive nature. One objective for this assignment is to prepare and submit documents according to the set assignment guidelines, allowing me to know all the ins and outs of formatting in Microsoft word. Scientific plots will be presented in a professional manner to display a comprehensive level of learning. In assignments pre and post reflections will be composed in order to see how the assignments fit into the world outside of the classroom. I will apply the ASU library and online databases to technical documents. The use of sources comes along with the risk of plagiarism, thus in this assignment I will expand my knowledge of

SOME TIBCO SOFTWARE EMBEDS OR BUNDLES OTHER TIBCO SOFTWARE. USE OF SUCH EMBEDDED OR BUNDLED TIBCO SOFTWARE IS SOLELY TO ENABLE THE FUNCTIONALITY (OR PROVIDE LIMITED ADD-ON FUNCTIONALITY) OF THE LICENSED TIBCO SOFTWARE. THE EMBEDDED OR BUNDLED SOFTWARE IS NOT LICENSED TO BE USED OR ACCESSED BY ANY OTHER TIBCO SOFTWARE OR FOR ANY OTHER PURPOSE. USE OF TIBCO SOFTWARE AND THIS DOCUMENT IS SUBJECT TO THE TERMS AND CONDITIONS OF A LICENSE AGREEMENT FOUND IN EITHER A SEPARATELY EXECUTED SOFTWARE LICENSE AGREEMENT, OR, IF THERE IS NO SUCH SEPARATE AGREEMENT, THE CLICKWRAP END USER LICENSE AGREEMENT WHICH IS DISPLAYED DURING DOWNLOAD OR

Exemplification is always needed to support argument or opinion and generally it was offered. One question,

Throughout the introduction I have used information from sites reference [1] - [4] found in the appendix.

7 2.1 2.2 2.3 2.4 Objectives .................................................................................................. 8 Delimitations ............................................................................................. 9 Target group .............................................................................................. 9 Report outline ......................................................................................... 10

Table of contents: Abstract ....................................................................................................................................... i Tables and Figures...................................................................................................................... v Chapter 1 Introduction............................................................................................................... vi Preface

Owing to rapid developments in digital technologies, the use of electronic media to capture, process and accumulate the information is witnessing an extraordinary development [1]. The stored information is reaching zeta-bytes [2], whereas our capability to scrutinize such large amount of data lags far behind the growth. One of the impediments is the high dimensionality of the datasets. This includes information in different application areas, such as in electronic health records (EHRs), biology, astronomy, medical imaging, video archiving, and web data. Different data mining techniques have been used to extract knowledge available in some of these data sets, albeit with limited success [3].

|classification system, even at their best, are still very much in their infancy. But, while they may still suffer from the |

Many researchers have proposed various methodologies for finding best solution. J. Ross Quinlan. In machine learning community, the decision tree algorithms, Quinlan’s ID3 and its successor C4.5: Programs for machine learning are probably the most popular. The various issues related to decision tree are discussed from the initial state of building a tree to methods of pruning, converting trees into rules and handling other problems such as missing attribute values. Apart from that, Quinlan discusses limitations of programs for machine learning, such as its bias in favour of rectangular regions along with ideas for extending the abilities of algorithm. [1]

• Your answer report should be prepared in WORD and printed in A4 or letter size sheets.

The classification is the final process which classifies the result (I.e, Normal, Abnormal etc). There are various classifiers used in literature which divides into two classes majorly called as dichotomies and some classifies into multi classes (e.g. decision trees [17], feedforward neural networks).

45 encompasses the measures to handle class imbalance. Most of the neural network based classifiers like FCRBF [4, 3],CCELM[9] minimizes least square error

PAGE 1 2 2.1 2.2 3 3.1 3.2 3.3 3.4 3.4.1 3.5 4 4.1 4.2 4.3 4.4 4.5 4.6 4.6.1 4.6.2 4.7 5 5.1 5.2 5.3 5.4 6 7 8 8.1 8.2 8.3 8.4 8.4.1 8.5 8.6 8.6.1 8.6.2 8.6.3 8.6.4 8.6.5 8.6.6 8.6.7 8.6.8 8.6.9 INTRODUCTION PURPOSE AND OUTCOMES OF THE MODULE Purpose Outcomes LECTURERS AND CONTACT DETAIL Lecturers Department University Contact with the university Unisa contact via e-mail Unisa’s need to contact you MODULE-RELATED RESOURCES Prescribed book Recommended books Electronic reserves (e-Reserves)

DUE DATE: 01/04/2015 FIGURES Figure 4.0.1 …………. ……………………………………………………………….. Page 6 Figure 5.0.1 …………. ……………………………………………………………….. Page 10 TABLES Figure 8.0. …………. ……………………………………………………………….. Page 15 Table of Contents FIGURES i TABLES ii 1.0.

Post-classification or reassessment of a classed image is performed to refine the classified image. Year