This paper describes a machine learning method, called Regression by Feature Projections (RFP), for predicting a real-valued target feature. In RFP training is based on simply storing the projections of the training instances on each feature separately. Prediction is computed through two approximation procedures. The first approximation process is to find the individual predictions of features ...

Our goal in this paper is to examine the application of Voronoi diagrams, a fundamental concept of computational geometry, to the nearest neighbor algorithm used in machine learning. We consider the question “Given a planar polygonal tessellation T and an integer k, is there a set of k points whose Voronoi diagram contains every edge in T?” We show that this question is NP-hard. We encountered ...

The nearest neighbor classifier (NNC) is a popular non-parametric classifier. It is a simple classifier with no design phase and shows good performance. Important factors affecting the efficiency and performance of NNC are (i) memory required to store the training set, (ii) classification time required to search the nearest neighbor of a given test pattern, and (iii) due to the curse of dimensi...

This paper describes the participation of LIG lab, in the batch filtering task for the INFILE (INformation FILtering Evaluation) campaign of CLEF 2009. As opposed to the online task, where the server provides the documents one by one, all of the documents are provided beforehand in the batch task, which explains the fact that feedback is not possible in the batch task. We propose in this paper ...

The aim of this study is to develop an automatic computer method to discriminate asymptomatic from knee osteoarthritis pathological gait patterns using ground reaction forces. We investigate a discriminant feature of kinetic data based on wavelets. Classification is based on the nearest neighbor classifier. Experiments were conducted using data of 43 cases, 16 asymptomatic and 27 pathological. ...

Classifier performance, particularly of instance-based learners such as k-nearest neighbors, is affected by the presence of noisy data. Noise filters are traditionally employed to remove these corrupted data and improve the classification performance. However, their efficacy depends on the properties of the data, which can be analyzed by what are known as data complexity measures. This paper st...

Recommender systems could be seen as an application of a data mining process in which data collection, pre-processing, building user profiles and evaluation phases are performed in order to deliver personalised recommendations. Collaborative filtering systems rely on user-to-user similarities using standard similarity measures. The symmetry of most standard similarity measures makes it difficul...

The number of Neighbours (k) and distance measure (DM) are widely modified for improved kNN performance. This work investigates the joint effect these parameters in conjunction with dataset characteristics (DC) on Euclidean; Chebychev; Manhattan; Minkowski; Filtered distances, eleven k values, four DC, were systematically selected parameter tuning experiments. Each experiment had 20 iterations,...

There are certain problems in machine learning which desire special attention when we scale up the size of the data or move towards data mining. One of them is the problem of searching nearest neighbours of a given point in k dimensional space. If the space is <k than k-d trees can solve the problem in asymptotically optimal time under certain conditions. We investigate the use of k-d trees in ...