Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

Impact of Urban Land-Cover Classification on Groundwater Recharge Uncertainty

Abstract: Objective and detailed mapping of urban land-cover types over large areas is important for hydrological modelling, as most man-made land-cover consist of sealed surfaces which strongly reduce groundwater recharge. Moreover, impervious surfaces are the predominant type in urbanized areas and can lead to increased surface runoff. Classification of man-made objects in urbanized areas is not straightforward due to similarity in spectral properties. This study examines the use of hyperspectral CHRIS-Proba images for complex urban land-cover classification of the Woluwe River catchment, Brussels, Belgium. Two methods are compared: 1) a multiscale region-based classification approach, which is based on a causal Markovian model being defined on a Multiscale Region Adjacency Tree and a set of nonparametric dissimilarity measures; and 2) a pixel based classification method with a Mahalanobis distance classifier. Multiscale region-based classification results in a Kappa value of 0.95 while pixel-based classification has a slightly lower Kappa value of 0.92. The impact of the classification method on the hydrology is estimated with the application of the WetSpass physically-based distributed water balance model. The model uncertainty is assessed with the use of a Monte Carlo simulation. Model results show that the region-based classification yields to a higher yearly recharge than the pixel-based classification. The overall uncertainty, quantified by the Monte Carlo method is lower for the region-based classification than for the pixel-based classification. The presented study indicates that the selection of the classification technique is of critical importance for the outcome of hydrological models.

Incremental collaborative filtering based on Mahalanobis distance and fuzzy membership for recommender systems

Abstract: Recommender systems, as an effective personalization approach, can suggest best-suited items (products or services) to particular users based on their explicit and implicit preferences by applying information filtering technology. Collaborative filtering (CF) method is currently the most popular and widely adopted recommendation approach. It works by collecting user ratings for items in a given domain and by computing the similarity between the profiles of several users in order to recommend items. Current similarity measures and models updated by traditional model-based CF have, however, shortcomings with respect to accuracy of prediction and scalability of recommender systems. To overcome these problems, here an incremental CF algorithm based on the Mahalanobis distance is presented. The algorithm has two phases: the learning phase, in which models of similar users are constructed incrementally, and the prediction phase, in which interested users are clustered by measuring their similarity to existing c...

A practical method for outlier detection in autoregressive time series modelling

Abstract: A practical method is developed for outlier detection in autoregressive modelling. It has the interpretation of a Mahalanobis distance function and requires minimal additional computation once a model is fitted. It can be of use to detect both innovation outliers and additive outliers. Both simulated data and real data re used for illustration, including one data set from water resources.

A novel approach for classifying imbalance welding data: Mahalanobis genetic algorithm (MGA)

Abstract: Feature selection from imbalance data plays an important role in building efficient support decision systems, improving the machine learning process performance and enhancing the classification accuracy. The problem of feature selection becomes even more difficult with imbalance data, which occurs in real-world domains when the classes representing the data set are not equally distributed. Using the traditional classifiers to seek an accurate performance over a full range of instances is not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into one class. In this paper, the Mahalanobis genetic algorithm (MGA) classifier is proposed to address the problem of feature selection for imbalance welding data. The MGA classifier was benchmarked with the Mahalanobis-Taguchi system (MTS) classifier, in terms of the following metrics: the total misclassification errors, the area under the curve (AUC) for receiver operating characteristic (ROC) curves, and the signal-to-noise (S/N) ratio. A real-life data set from the spot welding process was used as a pilot study. The results in terms of the total misclassification error and the AUC metrics showed that the MGA had better classification performance than MTS. Very close results were obtained when the training data set was balanced by using the Synthetic Minority Oversampling Technique (SMOTE) which indicates the suitability of the MGA and MTS classifiers to be used for the imbalance data set without using any preprocessor approach. Regarding the S/N ratio, the results were inconsistent with the other classification metrics, which raises the question about its credibility.