[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.

/pdf/spam-reviews-detection-in-the-time-of-covid-19-pandemic-1xz4gzca.pdf

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

The complexity of domain problem can slow or even hinder the learning process of neural networks. It is rather difficult to overcome such an obstacle because neural networks, as cited today in the literature, lack the interpretability of their internal structures. In this paper, we present a visualization approach capable of enhancing the understanding of neural networks. Our approach visualizes input and weight contributions, sensitivity analysis, and provides guidance in pruning less influential features and consequently reducing the complexity of domain problem while maintaining acceptable error rates. We conduct experiments on various datasets to show the effectiveness of our approach.

	 

	Key words: Neural network, visualization, input contribution, sensitivity analysis

/pdf/visualization-analysis-of-feed-forward-neural-network-input-4mxp6912ut.pdf

Visualization analysis of feed forward neural network input contribution

EvoCC framework is an open-source, free, and cross-platform framework implemented in Python which combines clustering, classification, and evolutionary computation methods. It optimizes the classification process by generating a classification model for each group generated by a clustering process where the clustering process is optimized by evolutionary optimization techniques. It includes the most well-known and recent nature-inspired metaheuristic optimization algorithms, well-known datasets, different fitness functions, and distance measures, and several well-known and highly-used classifiers. The aim is to provide the practitioners and researchers with a user-friendly and customizable implementation of classification-based nature-inspired optimization clustering algorithms that can be used by experienced and non-experienced users for the classification process in different domains. The current implementation of the framework includes eleven classification algorithms and five evaluation measures. It also utilizes the implementation of the EvoCluster framework which has ten metaheuristic optimizers, thirty datasets, five objective functions, more than twenty distance measures, and ten different ways for detecting the number of clusters (k value). The source code of EvoCC is publicly available at ( https://evo-ml.com/evocc/ ) 

EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python

Financial distress prediction is crucial in the financial domain because of its implications for banks, businesses, and corporations. Serious financial losses may occur because of poor financial distress prediction. As a result, significant efforts have been made to develop prediction models that can assist decision-makers to anticipate events before they occur and avoid bankruptcy, thereby helping to improve the quality of such tasks. Because of the usual highly imbalanced distribution of data, financial distress prediction is a challenging task. Hence, a wide range of methods and algorithms have been developed over recent decades to address the classification of imbalanced datasets. Metaheuristic optimization-based artificial neural networks have shown exciting results in a variety of applications, as well as classification problems. However, less consideration has been paid to using a cost sensitivity fitness function in metaheuristic optimization-based artificial neural networks to solve the financial distress prediction problem. In this work, we propose ENS_PSONNcost and ENS_CSONNcost: metaheuristic optimization-based artificial neural networks that utilize a particle swarm optimizer and a competitive swarm optimizer and five cost sensitivity fitness functions as the base learners in a majority voting ensemble learning paradigm. Three extremely imbalanced datasets from Spanish, Taiwanese, and Polish companies were considered to avoid dataset bias. The results showed significant improvements in the g-mean (the geometric mean of sensitivity and specificity) metric and the F1 score (the harmonic mean of precision and sensitivity) while maintaining adequately high accuracy.

https://www.mdpi.com/2076-3417/12/14/6918/pdf?version=1657269744

Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction

Theromostable lipases have wide range of biotechnological applications in the industry. Therefore, there is always high interest in investigating their features and operating conditions. However, Lipase production is a challenging and complex process due to its nature which is highly dependent on the conditions of the process such as temperature, initial pH, incubation period, time, inoculum size and agitation rate. Efficient optimization of the process is a common goal in order to improve the productivity and reduce the costs. In this paper, we apply a Symbolic Regression Genetic Programming (GP) approach in order to develop a mathematical model which can predict the lipase activities in submerged fermentation (SmF) system. The developed GP model is compared with a neural network model proposed in the literature. The reported evaluation results show superiority of GP in modeling and optimizing the process.

http://www.sersc.org/journals/IJAST/vol63/3.pdf

Hossam Faris

Papers

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

Visualization analysis of feed forward neural network input contribution

EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python

Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction

On Symbolic Regression for Optimizing Thermostable Lipase Production