Evaluating the Ability to Use Contextual Features to Map Deprived Areas 'Slums' in Multiple Cities

Population living in deprived conditions continues to grow, highlighting the urgent need for accurate high-resolution maps and detailed statistics to plan interventions and monitor changes. Unfortunately, data on deprived areas or "slums"is often unavailable, incomplete, or outdated. Leveraging satellite imagery can offer timely, and consistent information on deprived areas over large area However, there are limited studies that use free and open source data that can be used to map deprived areas over large areas and across multiple cities. To address these challenges, this study examines a scalable and transferable modeling approach to map deprived areas using contextual features extracted from freely available Sentinel-2 data. Models were trained and tested on three Sub-Sahara cities: Lagos Nigeria, Accra Ghana, and Nairobi, Kenya. The results indicate that models in individual city achieved F1 scores from 0.78-0.95 for the three cities. Additionally, the results indicate that the proposed approach may allow for the ability to transfer models from city to city allowing for large area and across city mapping.</p

One-class classifiers based on entropic spanning graphs

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

Booster in High Dimensional Data Classification

Classification problems specified in high dimensional data with smallnumber of observation are generally becoming common in specific microarray data. In the time of last two periods of years, manyefficient classification standard models and also Feature Selection (FS) algorithm which isalso referred as FS technique have basically been proposed for higher prediction accuracies. Although, the outcome of FS algorithm related to predicting accuracy is going to be unstable over the variations in considered trainingset, in high dimensional data. In this paperwe present a latest evaluation measure Q-statistic that includes the stability of the selected feature subset in inclusion to prediction accuracy. Then we are going to propose the standard Booster of a FS algorithm that boosts the basic value of the preferred Q-statistic of the algorithm applied. Therefore study on synthetic data and 14 microarray data sets shows that Booster boosts not only the value of Q-statistics but also the prediction accuracy of the algorithm applied