Identifying Mislabeled Training Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

Reducing the Effects of Detrimental Instances

Not all instances in a data set are equally beneficial for inducing a model of the data. Some instances (such as outliers or noise) can be detrimental. However, at least initially, the instances in a data set are generally considered equally in machine learning algorithms. Many current approaches for handling noisy and detrimental instances make a binary decision about whether an instance is detrimental or not. In this paper, we 1) extend this paradigm by weighting the instances on a continuous scale and 2) present a methodology for measuring how detrimental an instance may be for inducing a model of the data. We call our method of identifying and weighting detrimental instances reduced detrimental instance learning (RDIL). We examine RIDL on a set of 54 data sets and 5 learning algorithms and compare RIDL with other weighting and filtering approaches. RDIL is especially useful for learning algorithms where every instance can affect the classification boundary and the training instances are considered individually, such as multilayer perceptrons trained with backpropagation (MLPs). Our results also suggest that a more accurate estimate of which instances are detrimental can have a significant positive impact for handling them.Comment: 6 pages, 5 tables, 2 figures. arXiv admin note: substantial text overlap with arXiv:1403.189

Resolving depth measurement ambiguity with commercially available range imaging cameras

Time-of-flight range imaging is typically performed with the amplitude modulated continuous wave method. This involves illuminating a scene with amplitude modulated light. Reflected light from the scene is received by the sensor with the range to the scene encoded as a phase delay of the modulation envelope. Due to the cyclic nature of phase, an ambiguity in the measured range occurs every half wavelength in distance, thereby limiting the maximum useable range of the camera. This paper proposes a procedure to resolve depth ambiguity using software post processing. First, the range data is processed to segment the scene into separate objects. The average intensity of each object can then be used to determine which pixels are beyond the non-ambiguous range. The results demonstrate that depth ambiguity can be resolved for various scenes using only the available depth and intensity information. This proposed method reduces the sensitivity to objects with very high and very low reflectance, normally a key problem with basic threshold approaches. This approach is very flexible as it can be used with any range imaging camera. Furthermore, capture time is not extended, keeping the artifacts caused by moving objects at a minimum. This makes it suitable for applications such as robot vision where the camera may be moving during captures. The key limitation of the method is its inability to distinguish between two overlapping objects that are separated by a distance of exactly one non-ambiguous range. Overall the reliability of this method is higher than the basic threshold approach, but not as high as the multiple frequency method of resolving ambiguity

Improving Automatic Content Type Identification from a Data Set

Data file layout inference refers to building the structure and determining the metadata of a text file. The text files dealt within this research are personal information records that have a consistent structure. Traditionally, if the layout structure of a text file is unknown, the human user must undergo manual labor of identifying the metadata. This is inefficient and prone to error. Content-based oracles are the current state-of-the-art automation technology that attempts to solve the layout inference problem by using databases of known metadata. This paper builds upon the information and documentation of the content-based oracles, and improves the databases of the oracles through experimentation