Data Science Commons^™

Full Interpretable Machine Learning Method With In-Line Coordinates, Hoang Phan

All Master's Theses

This thesis explores a new approach for machine learning classification task in 2-dimensional space (2-D ML) with In-line Coordinates. This is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. In-line coordinates method allows discovering n-D patterns in 2-D space without loss of n-D information using graph representation of n-D data in 2-D. Specifically, this thesis shows that it can be done with In-line Based Coordinates in different modifications, which are defined, including static and dynamic ones. Some classification and regression algorithms based on these In-line Coordinates were explored. Two successful cases …

Plant Species Identification In The Wild Based On Images Of Organs, Meghana Kovur

Graduate Theses, Dissertations, and Problem Reports (ETD)

Image-based plant species identification in the wild is a difficult problem for several reasons. First, the input data is subject to a very high degree of variability because it is captured under fully unconstrained conditions. The same plant species may look very different in different images, while different species can often appear very similar, challenging even the recognition skills of human experts in the field. The large intra-class and small inter-class image variability makes this a fine-grained visual classification problem. One way to cope with this variability and to reduce image background noise is to predict species based on the …

Identification And Classification Of Radio Pulsar Signals Using Machine Learning, Di Pang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pul- sar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.

The research work presented in this dissertation is focused on development of ma- chine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named Single- Pulse Event Group IDentification (SPEGID), which automatically identifies and clas- …

Using Text Mining And Machine Learning Classifiers To Analyze Stack Overflow, Taylor Morris

Dissertations, Master's Theses and Master's Reports

StackOverflow is an extensively used platform for programming questions. In this report, text mining and machine learning classifiers such as decision trees and Naive Bayes are used to evaluate whether a given question posted on StackOverflow will be closed or answered. While multiple models were used in the analysis, the performance for the models was no better than the majority classifier. Future work to develop better performing classifiers to understand why a question is closed or answered will require additional natural language processing or methods to address the imbalanced data.

Convolutional Neural Network On Tanned And Synthetic Leather Textures, Faadihilah Ahnaf Faiz, Ahmad Azhari

Knowledge Engineering and Data Science

Tanned leather is an output from complex processes called tanning. Leather tanning is an important step that used to protect the fiber or protein structure of animal’s skin. Another reason of tanning process is to prevent the animal’s skin from any defect or rot. After the tanning is complete, the leather can be applied to produce a wide variety of leather products. Thus, the leather prices usually more expensive because it takes longer time in process. Another way to get cheaper price is make non-animal leather that usually known as synthetic or imitation leather. The purpose of this paper is …

Machine Learning Approaches For Improving Prediction Performance Of Structure-Activity Relationship Models, Gabriel Idakwo

Dissertations

In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to assess the activity and properties of small molecules. In silico methods such as Quantitative Structure-Activity/Property Relationship (QSAR) are used to correlate the structure of a molecule to its biological property in drug design and toxicological studies. In this body of work, I started with two in-depth reviews into the application of machine learning based approaches and feature reduction methods to QSAR, and then investigated solutions to three common challenges faced in machine learning based QSAR studies.

First, to improve the prediction accuracy of learning …

Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam

Student Works (2020-2029)

One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. Implementation of DM techniques in different areas of civil engineering has recently given very good results. However, application of DM in SHM is not used as much as expected, thus, many challenges are still ahead. Therefore, it is necessary to develop the applicability of DM in SHM. …

A Unified Framework For Sparse Online Learning, Peilin Zhao, Dayong Wong, Pengcheng Wu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream classification task. Different from the existing online data stream classification techniques, we propose a unified Sparse Online Classification (SOC) framework. Based on SOC, we derive a second-order online learning algorithm and a cost-sensitive sparse online …

Data Mining And Image Classification Using Genetic Programming, Mahsa Shokri Varniab

Master of Science in Computer Science Theses

Genetic programming (GP), a capable machine learning and search method, motivated by Darwinian-evolution, is an evolutionary learning algorithm which automatically evolves computer programs in the form of trees to solve problems. This thesis studies the application of GP for data mining and image processing. Knowledge discovery and data mining have been widely used in business, healthcare, and scientific fields. In data mining, classification is supervised learning that identifies new patterns and maps the data to predefined targets. A GP based classifier is developed in order to perform these mappings. GP has been investigated in a series of studies to classify …

Opinion Analysis For Emotional Classification On Emoji Tweets Using The Naïve Bayes Algorithm, Siti Sendari, Ilham Ari Elbaith Zaeni, Dian Candra Lestari, Hanny Prasetya Hariyadi

Knowledge Engineering and Data Science

Opinion Analysis is a research study needed to social media, since the content could become a trending topic and has a significant impact on social life. One of the social media that have a big contribution to cyberspace and information development is Twitter. In the Twitter application, users can insert images that represent emotions, facial expressions, or icons. Emoji is a graphic symbol in the form of an image to express a thing, with the Emoji, a text can be read and understood according to its meaning because the image represents it. Of the several things that have been mentioned …

Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …

Watersheds For Semi-Supervised Classification, Aditya Challa, Sravan Danda, B. S.Daya Sagar, Laurent Najman

Journal Articles

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting …

Constructing Interactive Visual Classification, Clustering And Dimension Reduction Models For N-D Data, Boris Kovalerchuk, Dmytro Dovhalets

Computer Science Faculty Scholarship

The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent …

Two Influential Primate Classifications Logically Aligned, Nico M. Franz, Naomi M. Pier, Deeann M. Reeder, Mingmin Chen, Shizhuo Yu, Parisa Kianmajd, Shaun Bowers, Bertram Ludäscher

Computer Science Faculty Scholarship

Classifications and phylogenies of perceived natural entities change in the light of new evidence. Taxonomic changes, translated into Code-compliant names, frequently lead to name:meaning dissociations across succeeding treatments. Classification standards such as the Mammal Species of the World (MSW) may experience significant levels of taxonomic change from one edition to the next, with potential costs to long-term, large-scale information integration. This circumstance challenges the biodiversity and phylogenetic data communities to express taxonomic congruence and incongruence inways that both humans and machines can process, that is, to logically represent taxonomic alignments across multiple classifications.We demonstrate that such alignments are feasible for …

An Improved Smote Algorithm Based On Genetic Algorithm For Imbalanced Data Collection, Qiong Gu, Xian-Ming Wang, Zhao Wu, Bing Ning, Chun-Sheng Xin

Electrical & Computer Engineering Faculty Publications

Classification of imbalanced data has been recognized as a crucial problem in machine learning and data mining. In an imbalanced dataset, minority class instances are likely to be misclassified. When the synthetic minority over-sampling technique (SMOTE) is applied in imbalanced dataset classification, the same sampling rate is set for all samples of the minority class in the process of synthesizing new samples, this scenario involves blindness. To overcome this problem, an improved SMOTE algorithm based on genetic algorithm (GA), namely, GASMOTE was proposed. First, GASMOTE set different sampling rates for different minority class samples. A combination of the sampling rates …

Classification With Hidden Markov Model, Badreddine Benyacoub, Souad Elbernoussi, Abdelhak Zoglat, Ismail El Moudden

Research and Infrastructure Service Enterprise (RISE) Faculty Publications

Classification and statistical learning by hidden markov model has achieved remarkable progress in the past decade. They have been applied in many areas like speech recognition and handwriting recognition. However, learning by Hidden Markov Model (HMM) is still restricted to supervised problems. In this paper, we propose a new learning method based on HMM techniques estimations, to built a model for classification. The approach consists of evaluation of the probability to belonging in one group, given the observations by a linear classifier. Our developed algorithm is based on discrete states and discrete observations cases of HMM. Experimental results show that …

Taxonomy Development And Knowledge Representation Of Nurses' Personal Cognitive Artifacts, Sharon Mclane, James P Turley

Faculty, Staff and Student Publications

Nurses prepare knowledge representations, or summaries of patient clinical data, each shift. These knowledge representations serve multiple purposes, including support of working memory, workload organization and prioritization, critical thinking, and reflection. This summary is integral to internal knowledge representations, working memory, and decision-making. Study of this nurse knowledge representation resulted in development of a taxonomy of knowledge representations necessary to nursing practice.This paper describes the methods used to elicit the knowledge representations and structures necessary for the work of clinical nurses, described the development of a taxonomy of this knowledge representation, and discusses translation of this methodology to the cognitive …

Exploration Of Computational Methods For Classification Of Movement Intention During Human Voluntary Movement From Single Trial Eeg, Ou Bai, Peter Lin, Sherry Vorbach, Jiang Li, Steve Furlani, Mark Hallett

Electrical & Computer Engineering Faculty Publications

Objective: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).

Methods: Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis …

Application Of Cognitive Engineering Principles To The Redesign Of A Dichotomous Identification Key For Parasitology, Kimberly A. Smith-Akins, Sharon Mclane, Thomas M. Craig, Todd R. Johnson

Faculty, Staff and Student Publications

Dichotomous identification keys are used throughout biology for identification of plants, insects, and parasites. However, correct use of identification keys can be difficult as they are not usually intended for novice users who may not be familiar with the terminology used or with the morphology of the organism being identified. Therefore, we applied cognitive engineering principles to redesign a parasitology identification key for the Internet. We addressed issues of visual clutter and spatial distance by displaying a single question couplet at a time and by switching to the appropriate next couplet after the user made a choice. Our analysis of …