As the link between human microbiomes and health has become more established, the interest in applying statistical approaches to microbiome data to understand the mechanisms behind these links has grown. However, microbiome data is often of unmanageable size, and consequently, producing quality lower dimensional representations of samples is a significant...
The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....
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XiaoliFern
The problem of document classification has been widely studied in machine
This paper introduces an approach to text classification for semi-structured label systems that have poor performance with standard methods. With the perspective that perfect classification for such a system is unattainable, we demonstrate an automated procedure to isolate the learnable elements of the problem. Through analysis of an example dataset,...
In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their...
Many methods have been explored in the literature of multi-label learning, ranging from simple problem transformation to more complex method that capture correlation among labels. However, mostly all existing works do not address the challenge with incomplete label data. The goal of this project is to extend the work of...
In multi-instance multi-label (MIML) learning, datasets are given in the form of bags, each of which contains multiple instances and is associated with multiple labels. This paper considers a novel instance clustering problem in MIML learning, where the bag labels are used as background knowledge to help group instances into...
Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
In bioacoustics, automatic animal voice detection and recognition from audio recordings is an emerging topic for animal preservation. Our research focuses on bird bioacoustics, where the goal is to segment bird syllables from the recording and predict the bird species for the syllables. Traditional methods for this task addresses the...
Recent work in machine learning concerns the detection and identification of bird species from audio recordings of their vocalizations. Such analysis can yield valuable ecological information concerning the activity and distribution of species in the wild. Current species-identification methods require individual syllables of bird audio as input, but field-collected audio...