Abstract: Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.
Abstract: The goal of a network-based intrusion detection
system is to classify activities of network traffics into two major
categories: normal and attack (intrusive) activities. Nowadays, data
mining and machine learning plays an important role in many
sciences; including intrusion detection system (IDS) using both
supervised and unsupervised techniques. However, one of the
essential steps of data mining is feature selection that helps in
improving the efficiency, performance and prediction rate of
proposed approach. This paper applies unsupervised K-means
clustering algorithm with information gain (IG) for feature selection
and reduction to build a network intrusion detection system. For our
experimental analysis, we have used the new NSL-KDD dataset,
which is a modified dataset for KDDCup 1999 intrusion detection
benchmark dataset. With a split of 60.0% for the training set and the
remainder for the testing set, a 2 class classifications have been
implemented (Normal, Attack). Weka framework which is a java
based open source software consists of a collection of machine
learning algorithms for data mining tasks has been used in the testing
process. The experimental results show that the proposed approach is
very accurate with low false positive rate and high true positive rate
and it takes less learning time in comparison with using the full
features of the dataset with the same algorithm.
Abstract: Text document categorization involves large amount
of data or features. The high dimensionality of features is a
troublesome and can affect the performance of the classification.
Therefore, feature selection is strongly considered as one of the
crucial part in text document categorization. Selecting the best
features to represent documents can reduce the dimensionality of
feature space hence increase the performance. There were many
approaches has been implemented by various researchers to
overcome this problem. This paper proposed a novel hybrid approach
for feature selection in text document categorization based on Ant
Colony Optimization (ACO) and Information Gain (IG). We also
presented state-of-the-art algorithms by several other researchers.
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
three feature selection methods are evaluated: Random Selection,
Information Gain (IG) and Support Vector Machine feature selection
(called SVM_FS). We show that the best results were obtained with
SVM_FS method for a relatively small dimension of the feature
vector. Also we present a novel method to better correlate SVM
kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).