[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

Classification tasks often include, among the large number of features to be processed in the datasets, many irrelevant and redundant ones, which can even decrease the efficiency of classifiers. Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks of the high dimensionality of datasets and often has two conflicting objectives: The first function aims to maximize the classification performance or reduce the error rate of the classifier. In contrast, the second function is designed to minimize the number of features. However, the majority of wrapper FS techniques are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the memory concept and personal best of the universes. The experimental results and comparisons indicate that the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and maintain a minimum classification error rate when dealing with different datasets compared with the most popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.

/pdf/a-robust-multi-objective-feature-selection-model-based-on-ix6xq4w8ac.pdf

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

Customer defection or "churn" rate is critically important since it leads to serious business loss. Therefore, many telecommunication companies and operators have increased their concern about churn management and investigated statistical and data mining based approaches which can help in identifying customer churn. In this paper, a churn prediction framework is proposed aiming at enhancing the predictability of churning customers. The framework is based on combining two heuristic approaches; Fast Fuzzy C-Means (FFCM) and Genetic Programming (GP). Considering the fact that GP suffers three different major problems: sensitivity towards outliers, variable results on various runs, and resource expensive training process, FFCM was first used to cluster the data set and exclude outliers, representing abnormal customers’ behaviors, to reduce the GP possible sensitivity towards outliers and training resources. After that, GP is applied to develop a classification tree. For the purpose of this work, a data set was provided by a major Jordanian telecommunication mobile operator.

Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry

Ransomware analysis and detection has been recently applied using supervised and unsupervised machine learning approaches. Combining both approaches simplifies the complexity of the data and builds an expert classifier for each predicted cluster. In this paper, a hybrid approach for detecting ransomware is proposed. It combines evolutionary clustering approach using Grey Wolf Optimizer (GWO) with an ensemble of Support Vector Machine (SVM) classification on a dataset collected from Android benign and ransomware applications. Experiments are applied to identify the best number of clusters for the proposed approach on the selected dataset and to compare the results with those obtained from applying the SVM single classification approach. Results show that applying SVM-\(GWO_{k=2}\) approach outperforms the corresponding SVM classifier in terms of accuracy.

A Classification Approach Based on Evolutionary Clustering and Its Application for Ransomware Detection

This section first provides an overview of the machine learning field in artificial intelligence (AI). The most well-regarded classes of methods in AI are discussed to show where AI optimization algorithms and machine learning techniques fit in. Different types of learning are briefly covered as well including supervised, unsupervised, and reinforcement techniques. The last part of this chapter includes discussions on evolutionary machine learning, which is the focus of this book.

Introduction to Evolutionary Machine Learning Techniques

With the emergence of Web 2.0 technology and the expansion of on-line social networks, current Internet users have the ability to add their reviews, ratings and opinions on social media and on commercial and news web sites. Sentiment analysis aims to classify these reviews reviews in an automatic way. In the literature, there are numerous approaches proposed for automatic sentiment analysis for different language contexts. Each language has its own properties that makes the sentiment analysis more challenging. In this regard, this work presents a comprehensive survey of existing Arabic sentiment analysis studies, and covers the various approaches and techniques proposed in the literature. Moreover, we highlight the main difficulties and challenges of Arabic sentiment analysis, and the proposed techniques in literature to overcome these barriers.

Hossam Faris

Papers

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry

A Classification Approach Based on Evolutionary Clustering and Its Application for Ransomware Detection

Introduction to Evolutionary Machine Learning Techniques

Sentiment analysis for Arabic language: A brief survey of approaches and techniques