K-plus-nearest neighbor method development for credit scoring machine learning tasks

Improved of K-Nearest Neighbor Techniques in Credit Scoring

Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. Credit scoring models have been widely used by financial institutions to determine if loan customers belong to either a good applicant group or a bad applicant group. The advantages of using credit scoring mo...

Quantum Nearest-Neighbor Algorithms for Machine Learning

We present several quantum algorithms for performing nearest-neighbor learning. At the core of our algorithms are fast and coherent quantum methods for computing distance metrics such as the inner product and Euclidean distance. We prove upper bounds on the number of queries to the input data required to compute these metrics. In the worst case, our quantum algorithms lead to polynomial reducti...

Advantage of k-Nearest Neighbor Method for developing QSAR models

Learning k-Nearest Neighbor Naive Bayes for Ranking

Accurate probability-based ranking of instances is crucial in many real-world data mining applications. KNN (k-nearest neighbor) [1] has been intensively studied as an effective classification model in decades. However, its performance in ranking is unknown. In this paper, we conduct a systematic study on the ranking performance of KNN. At first, we compare KNN and KNNDW (KNN with distance weig...

K- Nearest Neighbor Algorithm for Instance Based Learning

Instance Based Learning (IBL) results in classifying a new instance by examining and comparing it to the rest of the instances in the dataset. An example of this type of learning is the K-Nearest Neighbor algorithm which is based on examining an average Euclidian distance of the nearest k neighbors' parameters given a certain situation.