Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms

In finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the loss caused due to different risks. Various factors affect house insurance claims, some of which contribute to formulating insurance policies including specific features that the house has. Machine Learning (ML) when brought into the field of insurance would enable seamless formulation of insurance policies with a better performance which will also save time. Various classification algorithms have been used since they have a long history and have also got some modifications for optimum functionality. To illustrate the performance of each of the ML algorithms that we used here, we analyzed an insurance dataset drawn from Zindi Africa competition which is said to be from Olusola Insurance Company in Lagos Nigeria. This study therefore, compares the performance of Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Kernel Support Vector Machine (kSVM), Naïve Bayes (NB), and Random Forest (RF) Regressors on a dataset got from Zindi.africa competition and their performances are checked using not only accuracy and precision metrics but also recall, and F1 score metrics, all displayed on the confusion matrix. The accuracy result shows that logistic regression and Kernel SVM both gave 78% but kSVM outperformed LR in precision with a percentage of 70.8% for kSVM and 64.8% for LR showing that kSVM offered the best result

Identification of DNA-binding protein based multiple kernel model

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/

Multi-view LS-SVM Regression for Black-Box Temperature Prediction in Weather Forecasting

© 2017 IEEE. In multi-view regression, we have a regression problem where the input data can be represented in multiple ways. These different representations are called views. The aim of multi-view regression is to increase the performance of using only one view by taking into account the information available from all views. In this paper, we introduce a novel multi-view regression model called Multi-View Least Squares Support Vector Machines (MV LS-SVM) regression. This model is formulated in the primal-dual setting typical to Least Squares Support Vector Machines (LS-SVM) where a coupling term is introduced in the primal objective. This form of coupling allows for some degree of freedom to model the different representations while being able to incorporate the information from all views in the training phase. This work was motivated by the challenge of predicting temperature in weather forecasting. Black-box weather forecasting deals with a large number of observations and features and is one of the most challenging learning task around. In order to predict the temperature in a city, the historical data from that city as well as from the neighboring cities are taking into account. In the past, the data for different cities were usually simply concatenated. In this work, we use MV LS-SVM to do temperature prediction by regarding each city as a different view. Experimental results on the minimum and maximum temperature prediction in Brussels, show the improvement of the multi-view method with regard to previous work and that this technique is competitive to the existing state-of-the-art methods in weather prediction.status: publishe