A bank customer credit evaluation based on the decision tree and the simulated annealing algorithm

C4.5 is a learning algorithm that adopts local search strategy, and it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a globally optimized algorithm and it avoids the drawbacks of C4.5. This paper proposes a new credit evaluation method based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed method is effective. © 2008 IEEE

Human action recognition based on key postures

University of Technology, Sydney. Faculty of Engineering and Information Technology.Human motion analysis has gained considerable interests in the computer vision area due to the large number of potential applications and its inherent complexity. Currently, human motion analysis is at an early stage. Its final aim is to generate an easy understanding, high level semantic description in a given scene. Human action recognition is an important step to the final aim of human motion analysis. Human Detection Human detection is part of the field of human motion analysis. The thesis looks at human detection. The thesis proposes a method using histogram of angles to discriminate pedestrians from vehicles. This proposed method is encouraged by the reality that humans are non-rigid objects, An angle formed by the centroid point and two bottom points for a human changes periodically while the angle for the vehicle is relatively static. In this part, this thesis also presents an approach to detect humans in static images. The thesis proposes an approach which uses human geometric features to fulfill the task. Human Action Recognition The thesis focuses on human action recognition. The thesis proposes what will be called a key postures based human action recognition approach. As we have known, human actions can be well described by a few important postures (called key postures) which are significantly different from each other and all other postures can be clustered to these key postures. Therefore, these key postures can be used to represent and to infer the corresponding human action. The benefit of using key postures to represent human action is to reduce computational complexity. The thesis proposes two methods for human action recognition based on key postures. One is a human action recognition based on shape features and the other one is action recognition based on Radon transforms. Both methods follow three steps to achieve action recognition. These steps are video processing, key posture extraction and action recognition. A two-step approach is proposed to extract key postures from preprocessed action video. These two steps are coarse selection and fine selection. Feature extraction and representation are discussed in both steps. After key postures are extracted from a video, key posture sequences are used to represent human actions. Each key posture sequence is regarded as an action template. In order to compare two action sequences, Dynamic Time Warping (DTW) is applied to determine the distance between the two action sequences. In the second method, in order to obtain key postures, the action sequences are extracted from the preprocessed silhouettes using Radon transforms. Then, an unsupervised cluster analysis is applied to Radon transforms to identify the key postures for each sequence. Such key postures are used in the subsequent training and testing procedure. Several benchmark classifiers are used in this work for action learning and classification. Author's Publications This thesis covers the research results conducted by the author while undertaking for the degree. Most of the results have been published in research papers in refereed publications which are listed in Author's Publication for Doctor of Philosophy (PhD)

PENERAPAN PARTICLE SWARM OPTIMAZATION UNTUK MENEN-TUKAN KREDIT KEPEMILIKAN RUMAH DENGAN MENGGUNAKAN ALGORITMA C4.5

In studies that have been done previously to determine ownership loan home. One of the methods of the most widely used method with a high degree of accuracy is the C4.5 algorithm. In conducting this study also used a method algorithm C4.5 and to improve the accuracy will be performed using the addition of particle swarm optimization method for the determination of credit ratings. Homeownership after testing the results obtained is a support vector machine produces a value of 91.93% accuracy and AUC value of 0.860 was then performed using particle swarm optimization method in which the attributes which originally totaled 8 predictor variables selected from eight attributes used. The results showed higher accuracy value that is equal to 94.15% and AUC value of 0.941. So as to achieve an increased accuracy of 2.22% and an increase in AUC of 0.081. By looking at the accuracy and AUC values, the algorithm of support vector machines based on particle swarm optimization and therefore is in the category of classification is very good. &nbsp

PEMILIHAN MODEL PENENTUAN KELAYAKAN PINJAMAN ANGGOTA KOPERASI BERDASARKAN ALGORITMA SUPPORT VECTOR MACHINE, GENETIC ALGORITHMS, DAN NEURAL NETWORK

Saat ini kredit/pinjaman merupakan salah satu sumber keuntungan bisnis yang dengan resiko tinggi. Banyak metode klasifikasi telah diusulkan dalam literatur untuk mengatasi masalah ini. Tapi kebanyakan tidak diterima oleh para ahli karena berbagai alasan. Kebutuhan untuk mengetahui dan membedakan antara anggota baik dan yang buruk perlu dibangun sehingga pihak yang berkepentingan dapat mengambil salah satu tindakan pencegahan terjadinya masalah kredit macet. Dalam penelitian ini dilakukan Support vector macine, Genetic Algorithms, dan Neural Network terhadap data Anggota yang mendapat pembiayaan kredit/pinjaman koperasi baik yang bermasalah dalam pembayaran angsurannya maupun tidak. Dari hasil pengujian dengan mengukur kinerja ketiga algoritma tersebut menggunakan metode pengujian Cross Validation, Confusion Matrix dan Kurva ROC, diketahui bahwa algoritma GA memiliki nilai accuracy paling tinggi, yaitu 85.25%, diikuti oleh metode SVM dengan accuracy sebesar 83.50% dan yang terendah adalah metode NN dengan nilai accuracy 74.75%. Nilai AUC untuk metode GA juga menunjukkan nilai tertinggi, yaitu 0.776 disusul metode SVM dengan nilai AUC sebesar 0.760, dan yang terendah adalah nilai AUC NN, yaitu 0.714. Melihat nilai AUC dari ketiga metode tersebut maka ketiganya termasuk kelompok klasifikasi cukup karena nilai AUC-nya antara 0.70-0.80. Kata kunci: Support vector machine, Genetixc Algorithms, Neural Network, Receiver Operating Charactheristic, Confusion Matri

KOMPARASI PENERAPAN ALGORITMA C45, KNN DAN NEURAL NETWORK DALAM PROSES KELAYAKAN PENERIMAAN KREDIT KENDARAAN BERMOTOR

. In the development of business,credit problems remain tobe studie reveal edinteresting. Most problems the system imposed b ythe bank but the problem occur spreci selyt the human resources to manage credit, either on itsrelationship with the consumer or the mistake in leasing the wrong predictions in assessing consumers who apply for credit. Some computers have a lot offiel dresear chconductedto reduce the credit risk of causing harm to the company. In this study a comparison algorithm C4.5, KNN and theneural network which is appliedto the data consumer who gets the credit worthiness of motor good receptionis problematic in the install mentpaymentor not. The current methodhas not beenable to determinethe appropriatedata mining. The process of counting to three algorithms and programsadded with rapidminer can produce data that isaccurate and useful for all parties especially bess finance to further simplify the system in terms of determining the credit acceptan cevehiclesn results obtained C45 turns algorithmis more accuratein comparison witht woother algorithms. Keywords: C4.5, KNN, neural network, RapidMiner, Data Minin