Measuring Interpretability for Different Types of Machine Learning Models

2018 ◽  
pp. 295-308 ◽  
Author(s):  
Qing Zhou ◽  
Fenglu Liao ◽  
Chao Mou ◽  
Ping Wang
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Different Types ◽  
Machine Learning Models

The Relationship Between Reductionism and Prediction in Psychiatry: A Survey

2021 ◽  
Author(s):  
Eren Asena ◽  
Henk Cremers
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Literature Review ◽  
Significant Role ◽  
Biological Psychiatry ◽  
Learning Models ◽  
Different Types ◽  
Survey Results ◽  
The Relationship ◽  
Machine Learning Models

Introduction. Biological psychiatry has yet to find clinically useful biomarkers despite mucheffort. Is this because the field needs better methods and more data, or are current conceptualizations of mental disorders too reductionistic? Although this is an important question, there seems to be no consensus on what it means to be a “reductionist”. Aims. This paper aims to; a) to clarify the views of researchers on different types of reductionism; b) to examine the relationship between these views and the degree to which researchers believe mental disorders can be predicted from biomarkers; c) to compare these predictability estimates with the performance of machine learning models that have used biomarkers to distinguish cases from controls. Methods. We created a survey on reductionism and the predictability of mental disorders from biomarkers, and shared it with researchers in biological psychiatry. Furthermore, a literature review was conducted on the performance of machine learning models in predicting mental disorders from biomarkers. Results. The survey results showed that 9% of the sample were dualists and 57% were explanatory reductionists. There was no relationship between reductionism and perceived predictability. The estimated predictability of 11 mental disorders using currently available methods ranged between 65-80%, which was comparable to the results from the literature review. However, the participants were highly optimistic about the ability of future methods in distinguishing cases from controls. Moreover, although behavioral data were rated as the most effective data type in predicting mental disorders, the participants expected biomarkers to play a significant role in not just predicting, but also defining mental disorders in the future.

scholarly journals Machine Learning Based Prediction of Complex Bugs in Source Code

2019 ◽  
pp. 26-37
Author(s):  
Ishrat-Un-Nisa Uqaili ◽  
Syed Nadeem Ahsan
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Source Code ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Software Module ◽  
Different Types ◽  
Software Modules ◽  
Highly Correlated ◽  
Machine Learning Models

During software development and maintenance phases, the fixing of severe bugs are mostly very challenging and needs more efforts to fix them on a priority basis. Several research works have been performed using software metrics and predict fault-prone software module. In this paper, we propose an approach to categorize different types of bugs according to their severity and priority basis and then use them to label software metrics’ data. Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules. Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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