Sentence examples for classification improvement from inspiring English sources
The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested.
In the first of the discussed models the potential for combined classification improvement by GA-selected weights for the soft combining of classifier outputs has been investigated.
The method combines two techniques: (1) the Histogram Region of Interest Fixed by Thresholds (HRIFT) is designed to capture the color information of INIs for basic feature extraction; (2) an automated feature synthesis, based on the HRIFT features, is designed to identify OPMD by means of Genetic Programming and the Expectation Maximization algorithm (GP-EM) for classification improvement.
Secondary endpoints were calibration and net classification improvement when the biomarkers were combined with the clinical pulmonary infection score [10].
The aim from the three stages is to explore the possible aspects of binary classification improvement through aggregation.
These advances can be used in a wide range of signal processing applications from Telecommunications to Biomedicine including EEG and Fetal Movement signals analysis and processing, where time-frequency peak detectors can provide additional features for classification improvement.
In Section 3, the classification improvements are presented and discussed.
Table 7 shows the five datasets, which yield classification improvements using the distance-based features.
For example, in cases where there was only one source of distortion, classification improvements of up to 8%and10%0% were achieved under babble noise and AWGN, respectively, using the SCF feature.
Then, the experiments using naïve Bayes, k-NN, and SVM classifiers over ten various domain datasets show that concatenating the original features with the distance-based features can provide some level of classification improvements over the chosen datasets except high-dimensional image related datasets.
With the classification improvements, we conclude that the effective use of microRNA target and gene network information can enhance the performance of GS.
