Emerging Paradigms in Machine Learning: An Introduction

Ramanna, Sheela; Jain, Lakhmi C.; Howlett, Robert J.

doi:10.1007/978-3-642-28699-5_1

Cited by 5 publications

(8 citation statements)

References 20 publications

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“…In order to predict the real-valued CN using classification techniques, the continuous domain was mapped onto a finite set of categories II. Two different criteria were used to generate sets with two, three and five classes (or states) to form classes with balanced and imbalanced class distribution, uniform frequency and uniform length, respectively 71,72…”

Section: Methodsmentioning

confidence: 99%

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Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Arana‐Daniel

Álvarez-Gallegos

López-Franco

et al. 2016

Evol Bioinform Online

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With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

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Section: Methodsmentioning

confidence: 99%

“…II Two different criteria were used to generate sets with two, three and five classes (or states) to form classes with balanced and imbalanced class distribution, uniform frequency and uniform length, respectively. 71,72…”

Section: Methodsmentioning

confidence: 99%

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Arana‐Daniel

Álvarez-Gallegos

López-Franco

et al. 2016

Evol Bioinform Online

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“…Specification mining has been studied in different domains such as hardware design, web services, programming languages and software engineering. Most of these techniques are targeted towards a narrow class of specifications, such as decision-tree learning [11] (which produces a single output for a sequence of decisions) or PROLOG/LISP learning techniques [16] which use declarative languages. These techniques introduce a learning bias and restrict mined specifications to mostly functional properties.…”

Section: Introductionmentioning

confidence: 99%

Putting humpty-dumpty together: Mining causal mechanistic biochemical models from big data

Hussain

Velasquez

Sassano

et al. 2014

2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

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In traditional engineering disciplines, the construction of a system is usually preceded by a formal or informal specification of the design of the system being developed. In biochemical applications, however, a detailed specification of the system's structure and dynamics is usually unavailable. Thus, mechanistic details of biochemical systems must be mined from experimental observations. In this paper, we adopt a formal methods approach towards deriving causal mechanistic models from time-series observations of biochemical systems. The mined model captures causality among multiple biological events and also allows causal relationships between sets of events. We exploit results from trace theory and use the power of powerful constraint solvers to develop a new framework for causality identification and reasoning that captures dynamic relationships among species in biochemical reaction networks.

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“…In particular, supervised and semi-supervised machine learning algorithms [9, 10] have been extensively investigated for automatically identifying related cells.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods have been deployed for automatically labeling subpopulations of cells in flow cytometry data sets – a process popularly referred to as gating. In particular, supervised and semi-supervised machine learning algorithms [ 9 , 10 ] have been extensively investigated for automatically identifying related cells.…”

Section: Introductionmentioning

confidence: 99%

A theorem proving approach for automatically synthesizing visualizations of flow cytometry data

et al. 2017

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BackgroundPolychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset.ResultsThis paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections.ConclusionsWe describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data.

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Emerging Paradigms in Machine Learning: An Introduction

Cited by 5 publications

References 20 publications

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Putting humpty-dumpty together: Mining causal mechanistic biochemical models from big data

A theorem proving approach for automatically synthesizing visualizations of flow cytometry data

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