Naive Bayes classifier

About: Naive Bayes classifier is a research topic. Over the lifetime, 16207 publications have been published within this topic receiving 386597 citations. The topic is also known as: Naive Bayes.

Mammographic mass classification using filter response patches

Abstract: Considering the importance of early diagnosis of breast cancer, a supervised patch-wise texton-based approach has been developed for the classification of mass abnormalities in mammograms. The proposed method is based on texture-based classification of masses in mammograms and does not require segmentation of the mass region. In this approach, patches from filter bank responses are utilised for generating the texton dictionary. The methodology is evaluated on the publicly available Digital Database for Screening Mammography database. Using a naive Bayes classifier, a classification accuracy of 83% with an area under the receiver operating characteristic curve of 0.89 was obtained. Experimental results demonstrated that the patch-wise texton-based approach in conjunction with the naive Bayes classifier constructs an efficient and alternative approach for automatic mammographic mass classification.

TSCBAS: A Novel Correlation Based Attribute Selection Method and Application on Telecommunications Churn Analysis

Abstract: Attribute selection has a significant effect on the performance of the machine learning studies by selecting the attributes having significant effect on result, reducing the number of attributes, and reducing the calculation cost. In this study, a new attribute selection method which is a combination of the R-correlation coefficient-based attribute selection (RCBAS) and the ρ-correlation coefficient-based attribute selection (ρCBAS) called the Two-Stage Correlation-Based Attribute Selection (TSCBAS) is proposed to select significant attributes. The proposed attribute selection method has been applied to customer churn prediction on a telecommunications dataset for performance evaluation. The dataset used in the study includes real customer call records details for the years 2013 and 2014 obtained from a major telecommunications company in Turkey. Apart from the proposed attribute selection method, four different methods named Rcorrelation coefficient-based attribute selection, ρ-correlation coefficient-based attribute selection, ReliefF, and Gain Ratio have been used for creating five datasets. After that, four classifier algorithms including Random Forest, C4.5 Decision Tree, Naive Bayes and AdaBoost.M1 have been applied. The obtained results have been compared according to the performance metrics comprising Accuracy (ACC), Sensitivity (TPR), Specificity (SPC), F-measure (F), AUC (area under the ROC curve), and run-time. The results of the comparisons show that the proposed attribute selection algorithm outperforms the state of the art methods on customer churn prediction.

Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

Abstract: Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naive Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naive Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naive Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%.

A novel multilabel classification of remote sensing images using XGBoost

Abstract: Monitoring the land cover from space can help us in many ways such as understanding the natural phenomenon, human activities and managing the natural resources. The problem of classifying a satellite image-chip, into one class or multiple classes, is explored. These classes could be land cover, natural phenomenon or human activity. The classification problem is solved using multilabel classification framework. The used data is obtained from Kaggle, and explored an effective techniques inspired from transfer learning (which utilizes convolution neural network (CNN) as feature extractor) and a popular gradient boosting algorithm: XGBoost (as classifier). The performance of designed model is evaluated through the K-fold cross validation. The proposed technique gives F1-score of 0.87411, which is far better than state-of-the-art methods, like Random Forest and Gaussian Naive Bayes.