Improving the Operation of Text Categorization Systems with Selecting Proper Features Based on PSO-LA

With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently

A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation

Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods

Sentiment Polarity Classification of Comments on Korean News Articles Using Feature Reweighting

일반적으로 인터넷 신문 기사에 대한 댓글은 그 신문 기사에 대한 주관적인 감정이나 의견을 포함하고 있다. 따라서 이런 신문 기사의 댓글에 대한 감정을 인식하고 분류하는 데에는 그 신문 기사의 원문 내용이 중요한 영향을 미친다. 이런 점에 착안하여 본 논문은 기사의 원문 내용과 감정 사전을 이용하는 가중치 조정 방법을 제안하고, 제안된 가중치 조정 방법을 이용해서 한국어 신문 기사의 댓글에 대한 감정 이진 분류 방법을 제안한다. 가중치 조정 방법에는 다양한 자질 집합이 사용되는데 그것은 댓글에 포함된 감정 단어, 그리고 감정 사전과 뉴스 기사의 본문에 관련된 자질들, 마지막으로 뉴스 기사의 카테고리 정보가 포함되어 있다. 여기서 말하는 감정 사전은 한국어 감정 사전을 의미하며 아직 공개된 것이 없기 때문에, 기존에 있는 영어 감정 사전을 이용하여 구축하였다. 본 논문에서 제안된 감정 이진 분류는 기계 학습을 이용한다. 일반적으로 기계 학습을 위해서는 학습 말뭉치가 필요한데 특별히 감정 분류 문제에서는 긍정 혹은 부정 감정 태그가 부착된 말뭉치가 필요하다. 이 말뭉치의 경우도, 공개된 한국어 감정 말뭉치가 아직 없기 때문에 말뭉치를 직접 구축하였다. 사용된 기계 학습 방법으로는 Na&iumlve Bayes, k-NN, SVM이 있고, 자질 선택 방법으로는 Document Frequency, χ^2 statistic, Information Gain이 있다. 그 결과, 댓글 안에 포함된 감정 단어와 그 댓글에 대한 기사 본문이 감정 분류에 매우 효과적인 자질임을 확인할 수 있었다.Chapter 1 Introduction 1 Chapter 2 Related Works 4 2.1 Sentiment Classification 4 2.2 Feature Weighting in Vector Space Model 5 2.3 Feature Extraction and Selection 7 2.4 Classifiers 10 2.5 Accuracy Measures 14 Chapter 3 Feature Reweighting 16 3.1 Feature extraction in Korean 16 3.2 Feature Reweighting Methods 17 3.3 Examples of Feature Reweighting Methods 18 Chapter 4 Sentiment Polarity Classification System 21 4.1 Model Generation 21 4.2 Sentiment Polarity Classification 23 Chapter 5 Data Preparation 25 5.1 Korean Sentiment Corpus 25 5.2 Korean Sentiment Lexicon 27 Chapter 6 Experiments 29 6.1 Experimental Environment 29 6.2 Experimental Results 30 Chapter 7 Conclusions and Future Works 38 Bibliography 40 Acknowledgments 4

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity