In medical practice, the diagnosis or prediction models requiring complicated computations are not widely recognized due to difficulty in interpreting the course of reasoning and the complexity of computations. Medical personnel have used the nomograms which are a graphical representation for numerical relationships that enables to easily compute a complicated function without help of computati...

Many medical diagnosis applications are characterized by datasets that contain under-represented classes due to the fact that the disease is much rarer than the normal case. In such a situation classifiers such as decision trees and Naïve Bayesian that generalize over the data are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far ar...

In this paper, we introduce a new approach to the classification of streaming data based on bootstrap aggregation (bagging). The proposed approach creates an ensemble model by using ID3 classifier, naïve Bayesian classifier, and k-Nearest-Neighbor classifier for a learning scheme where each classifier gives the weighted prediction. ID3, naïve Bayesian, and k-NearestNeighbor classifiers are very...

In this work, we analyse the Electroencephalogram (EEG) and tactile signals acquired during dynamic exploration of objects of seven different geometric shapes and observe that classification performance using features from both the domains together is better than using the either alone. Classification is done by Support Vector Machine and Naïve Bayesian (NB) classifiers using discrete wavelet t...

This paper proposes a two-step method for Chinese text categorization (TC). In the first step, a Naïve Bayesian classifier is used to fix the fuzzy area between two categories, and, in the second step, the classifier with more subtle and powerful features is used to deal with documents in the fuzzy area, which are thought of being unreliable in the first step. The preliminary experiment validat...

Recent works on ensemble methods like Adaptive Boosting have been applied successfully in many problems. Ada-Boost algorithm running on a given weak learner several times on slightly altered data and combining the hypotheses in order to achieve higher accuracy than the weak learner. This paper presents an expert system that boosts the performance of an ensemble of classifiers. In, Boosting, a s...

Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word el...

NEUOM: Identifying Opinionated Sentences in Chinese and English Text Zhang, Ke Wang, Muhua Zhu, Tong Xiao, Jingbo Zhu Natural Language Processing Lab, Northeastern University {zhangcl, xiaotong, zhujingbo}@mail.neu.edu.cn [email protected], [email protected] Abstract This paper introduces our NEUOM system which participates in the opinionated sentence detection task, one of evaluation task...