Associative classifier for uncertain data

Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. And uncertainty may render many conventional classifiers inapplicable to uncertain classification tasks. In this paper, based on U-Apriori algorothm and CBA algorithm, we propose an associative classifier for uncertain data, uCBA (uncertain Classification Based on Associative), which can classify both certain and uncertain data. The algorithm redefines the support, confidence, rule pruning and classification strategy of CBA. Experimental results on 21 datasets from UCI Repository demonstrate that the proposed algorithm yields good performance and has satisfactory performance even on highly uncertain data

Recognizing Uncertainty in Speech

We address the problem of inferring a speaker's level of certainty based on prosodic information in the speech signal, which has application in speech-based dialogue systems. We show that using phrase-level prosodic features centered around the phrases causing uncertainty, in addition to utterance-level prosodic features, improves our model's level of certainty classification. In addition, our models can be used to predict which phrase a person is uncertain about. These results rely on a novel method for eliciting utterances of varying levels of certainty that allows us to compare the utility of contextually-based feature sets. We elicit level of certainty ratings from both the speakers themselves and a panel of listeners, finding that there is often a mismatch between speakers' internal states and their perceived states, and highlighting the importance of this distinction.Comment: 11 page

River quality classification using different distances in k-nearest neighbors algorithm

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values of the river. However, due to huge data collection on river pollution with uncertain water quality parameter values, need to a different approach to classify the river quality. One of the supervised classification algorithms known as K-Nearest Neighbors (KNN) seems to give new approach for river quality classification where each data points are classified according to the k number or the closest data points neighbors. Therefore, the purpose of this paper is to apply different distances and distance-weighted in KNN for finding the most accurate river quality classification. The accuracy results are compared with Support Vector Machine (SVM) and Decision Tree (DT) algorithms. This KNN algorithm will give a different approach in classify the river quality