Survey Based Classification of Bug Triage Approaches

2016 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Asmita Yadav ◽  
Sandeep Kumar Singh
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Cold Start ◽  
Future Directions ◽  
Meta Data ◽  
Bug Triaging ◽  
Comprehensive Survey ◽  
Cold Start Problem

This paper presents a comprehensive survey of bug triaging approaches in three classes namely machine learning based, meta-data based and profile based. All approaches under three categories are critically compared and some potential future directions and challenges are reported. Findings from the survey show that there is a lot of scope to work in cold-start problem, developer- profiling, load balancing, and reopened bug analysis.

scholarly journals Survey Based Classification of Bug Triage Approaches

2016 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Asmita Yadav ◽  
Sandeep Kumar Singh
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Cold Start ◽  
Future Directions ◽  
Meta Data ◽  
Bug Triaging ◽  
Comprehensive Survey ◽  
Cold Start Problem

This paper presents a comprehensive survey of bug triaging approaches in three classes namely machine learning based, meta-data based and profile based. All approaches under three categories are critically compared and some potential future directions and challenges are reported. Findings from the survey show that there is a lot of scope to work in cold-start problem, developer- profiling, load balancing, and reopened bug analysis.

scholarly journals EXPRESS: Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

2021 ◽  
pp. 002224372110329
Author(s):  
Nicolas Padilla ◽  
Eva Ascarza
Keyword(s):  
Machine Learning ◽  
Cold Start ◽  
Customer Relationship ◽  
Exponential Families ◽  
Modeling Framework ◽  
Wide Range ◽  
The Moment ◽  
Probabilistic Machine Learning ◽  
Marketing Actions ◽  
Cold Start Problem

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

Evolving Learning in the Stuff Swamp

2003 ◽  
pp. 211-228 ◽  
Author(s):  
Jon Dron ◽  
Chris Boyne ◽  
Richard Mitchell
Keyword(s):  
Learning Environments ◽  
Cold Start ◽  
Learning Resources ◽  
Future Directions ◽  
Web Based ◽  
Natural Systems ◽  
Self Organisation ◽  
Emergent Structure ◽  
Cold Start Problem

This chapter describes the theory, background and some uses of CoFIND (Collaborative Filter in N Dimensions), a Web-based database of learning resources which is created by and for learners. CoFIND is designed to exploit principles of evolution and self-organisation to create an emergent structure to learning resources. Through the manipulation of learner-supplied metadata such as classifications and ratings, this structure shapes itself to the needs of the learners who create it, providing something akin to guidance traditionally supplied by a teacher. The chapter starts with a discussion of the weaknesses of existing means for groups of learners to discover learning resources including search engines, directories, seals of approval, and collaborative filters. It considers a range of methods by which self-organisation is achieved in natural systems (notably evolution and stigmergy) and which underpin the CoFIND system. CoFIND is described and examples are given of some of its uses. The authors discuss some issues which arise, especially its cold-start problem, influences of surrounding systems and the role of motivation. The chapter concludes with a discussion of potential future directions for CoFIND and identifies some other aspects of learning environments which may benefit from such a self-organising system.

scholarly journals Imbalance class problems in data mining: a review

2019 ◽  
Vol 14 (3) ◽  
pp. 1552 ◽  
Author(s):  
Haseeb Ali ◽  
Mohd Najib Mohd Salleh ◽  
Rohmat Saedudin ◽  
Kashif Hussain ◽  
Muhammad Faheem Mushtaq
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Minority Class ◽  
Future Directions ◽  
Significant Research ◽  
Comprehensive Survey ◽  
Imbalanced Class

<span>The imbalanced data problems in data mining are common nowadays, which occur due to skewed nature of data. These problems impact the classification process negatively in machine learning process. In such problems, classes have different ratios of specimens in which a large number of specimens belong to one class and the other class has fewer specimens that is usually an essential class, but unfortunately misclassified by many classifiers. So far, significant research is performed to address the imbalanced data problems by implementing different techniques and approaches. In this research, a comprehensive survey is performed to identify the challenges of handling imbalanced class problems during classification process using machine learning algorithms. We discuss the issues of classifiers which endorse bias for majority class and ignore the minority class. Furthermore, the viable solutions and potential future directions are provided to handle the problems<em>.</em></span>

scholarly journals Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems

2019 ◽  
Vol 137 ◽  
pp. 91-103 ◽  
Author(s):  
Konstantinos Pliakos ◽  
Seang-Hwane Joo ◽  
Jung Yeon Park ◽  
Frederik Cornillie ◽  
Celine Vens ◽  
...  
Keyword(s):  
Machine Learning ◽  
Item Response Theory ◽  
Item Response ◽  
Adaptive Learning ◽  
Cold Start ◽  
Learning Systems ◽  
Response Theory ◽  
Adaptive Learning Systems ◽  
Cold Start Problem

Machine Learning Classification of Low-grade and High-grade Chondrosarcomas Based on MRI-based Texture Analysis

2019 ◽  
Author(s):  
S. Gitto ◽  
D. Albano ◽  
V. Chianca ◽  
R. Cuocolo ◽  
L. Ugga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Low Grade ◽  
High Grade ◽  
Machine Learning Classification

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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