Currently available algorithms for data stream classification are all designed to handle precise data, while data with uncertainty or imperfection is quite natural and widely seen in real-life applications. Uncertainty can arise in attribute values as well as in class values. In this paper, we focus on the classification of streaming data that has different degrees of uncertainty within class v...

land cover is one of basic data layers in geographic information system for physical planning and environmentalmonitoring. digital image classification is generally performed to produce land cover maps from remote sensing data,particularly for large areas. in the present study the multispectral image from irs liss-iii image along with ancillary datasuch as vegetation indices, principal componen...

The identification and classification of the spine deformity play an important role when considering surgical planning for adolescent patients with idiopathic scoliosis. The subject of this article is the Lenke classification of scoliotic spines using Cobb angle measurements. The purpose is two-fold: (1) design a rulebased diagram to assist clinicians in the classification process and (2) inves...

The study was conducted with the objective of mapping landscape cover of Nechsar National park in Ethiopia to produce spatially accurate and timely information on land use and changing pattern. Monitoring provides the planners and decision-makers with required information about the current state of its development and the nature of changes that have occurred. Remote sensing and Geographical Inf...

Collective classification algorithms have been used to improve classification performance when network training data with content, link and label information and test data with content and link information are available. Collective classification algorithms use a base classifier which is trained on training content and link data. The base classifier inputs usually consist of the content vector ...

Beside the curse of dimensionality and imbalanced classes, unfavorable data distributions can hamper classification accuracy. This is particularly problematic with increasing dimensionality of the classification task. A classifier that can handle high-dimensional and imbalanced data sets is the cascade classification method for time series. The cascade classifier can compound unfavorable data d...

We propose a multi-objective machine learning approach guaranteed to find the Pareto optimal set of hybrid classification models consisting of comprehensible and incomprehensible submodels. The algorithm run-times are below 1 s for typical applications despite the exponential worst-case time complexity. The user chooses the model with the best comprehensibilityaccuracy trade-off from the Pareto...