Simple Naive Bayes Classifier Research Articles

Bardo: Emotion-Based Music Recommendation for Tabletop Role-Playing Games

In this paper we introduce Bardo, a real-time intelligent system to automatically select the background music for tabletop role-playing games. Bardo uses an off-the-shelf speech recognition system to transform into text what the players say during a game session, and a supervised learning algorithm to classify the text into an emotion. Bardo then selects and plays as background music a song representing the classified emotion. We evaluate Bardo with a Dungeons and Dragons (D&D) campaign available on YouTube. Accuracy experiments show that a simple Naive Bayes classifier is able to obtain good prediction accuracy in our classification task. A user study in which people evaluated edited versions of the D&D videos suggests that Bardo's selections can be better than those used in the original videos of the campaign.

A Novel Fuzzy Linguistic Fusion Approach to Naive Bayes Classifier for Decision Making Applications

Naive Bayes is one of the most widely used classifier algorithms in various data mining problems. The performance of the Naí¯ve Bayes Classifier is comparable to other classifiers as it yields impressive results in multiple applications. An increase in the performance of the Naive Bayes Classifier is possible by identifying and forming segments of the data handled by the classifier. In this paper, a novel fuzzy-based fusion approach to selected quantitative features is proposed. The approach is used to improve the prediction accuracy of the Naive Bayes Classifier (NBC). The linguistic computing model with fusion operators, using ranked indexes of the linguistic terms in the dataset is made use in this proposed approach. Fuzzy values are generated only for the numerical attributes in the initial phase using 2-tuple linguistic computations. The equivalent real value computations are performed in order to express the results in the initial domain of the expression. These computations ensure improved comprehensiveness of the results of the classifier. The model incorporates the concepts of linguistic terms, fuzzy logic, fusion methods, and aggregation operations to the classical Naí¯ve Bayes Classifier. Such incorporation is used to improve the performance of the classifier in various decision-making applications. The proposed model is validated using a standard benchmark dataset-Stat log Heart disease dataset. It is obtained from the UCI Machine Learning Repository. The proposed Linguistic Fuzzy Naive Bayes Classifier showed better accuracy compared to the Simple Naive Bayes Classifier performance.

Fusion of Nonintrusive Environmental Sensors for Occupancy Detection in Smart Homes

We presented an approach to detect and count occupants using a fusion of environmental sensors from an indoor air quality measurement system. Environmental sensors, as opposed to motion detectors, are nonintrusive, easy to install, low cost, detect nonmoving occupants, do not have dead spots, and can even infer the number of occupants. For this paper, we conducted measurements of carbon dioxide, volatile organic compounds (VOCs), air temperature, and air relative humidity in four student apartments for a total of 49 days. We extracted features from the environmental sensors and selected subsets using correlation-based feature selection. Subsequently, we performed a comparison of the supervised learning models repeated incremental pruning to produce error reduction, naive Bayes (NB), C4.5 decision tree, logistic regression, ${k}$ -nearest neighbors, and random forest. We further proposed a method to greatly reduce time and effort of collecting training data in residential buildings. The results indicated that the predictive power of VOC sensing is comparable to that of carbon dioxide. With a simple NB classifier, our approach detected occupancy and estimated the number of occupants with an accuracy of 81.1 % and 64.7 %, respectively.

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust to the sensor's orientation. Our approach comprises four main steps. First, 3D feature vectors are selected since they are theoretically independent of orientation. Second, the least interesting features are suppressed to speed up computation and increase robustness against overfitting. Third, the features are further selected through an embedded method, which selects features through simultaneous feature selection and classification. Finally, feature sets are optimized through 10-fold cross-validation. We collected real-world data through multiple sensors around the neck of five goats. The results show that activities can be accurately recognized using only accelerometer data and a few lightweight features. Additionally, we show that the performance is robust to sensor orientation and position. A simple Naive Bayes classifier using only a single feature achieved an accuracy of 94 % with our empirical dataset. Moreover, our optimal feature set yielded an average of 94 % accuracy when applied with six other classifiers. This work supports embedded, real-time, energy-efficient, and robust activity recognition for animals.

Text-independent Speaker Identification Using Soft Bag-of-Words Feature Representation

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

Implementing Data Mining Algorithms in Microsoft SQL Server

The OLE DB for DM (Microsoft’s object-based technology for sharing information and services across process and machine boundaries focused on database mining applications) specification provides an industry standard for implementation of data mining algorithms aggregated with Microsoft SQL Server 2000. The Simple Naive Bayes classifier is implemented using the OLE DB for DM Resource Kit. Numeric input attributes, multiple prediction trees and incremental classification are considered. All necessary steps to implement this algorithm are explained and discussed. Some results are shown to illustrate the capabilities of the implementation.

Assessing the limits of genomic data integration for predicting protein networks

Genomic data integration--the process of statistically combining diverse sources of information from functional genomics experiments to make large-scale predictions--is becoming increasingly prevalent. One might expect that this process should become progressively more powerful with the integration of more evidence. Here, we explore the limits of genomic data integration, assessing the degree to which predictive power increases with the addition of more features. We focus on a predictive context that has been extensively investigated and benchmarked in the past-the prediction of protein-protein interactions in yeast. We start by using a simple Naive Bayes classifier for integrating diverse sources of genomic evidence, ranging from coexpression relationships to similar phylogenetic profiles. We expand the number of features considered for prediction to 16, significantly more than previous studies. Overall, we observe a small, but measurable improvement in prediction performance over previous benchmarks, based on four strong features. This allows us to identify new yeast interactions with high confidence. It also allows us to quantitatively assess the inter-relations amongst different genomic features. It is known that subtle correlations and dependencies between features can confound the strength of interaction predictions. We investigate this issue in detail through calculating mutual information. To our surprise, we find no appreciable statistical dependence between the many possible pairs of features. We further explore feature dependencies by comparing the performance of our simple Naive Bayes classifier with a boosted version of the same classifier, which is fairly resistant to feature dependence. We find that boosting does not improve performance, indicating that, at least for prediction purposes, our genomic features are essentially independent. In summary, by integrating a few (i.e., four) good features, we approach the maximal predictive power of current genomic data integration; moreover, this limitation does not reflect (potentially removable) inter-relationships between the features.