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The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
Breast cancer is invasive cancer among world's women above 35 years of age. The most common symptoms of breast cancer are lumps, change in shape/skin colour and liquid oozing out from nipple. Breast cancer mostly starts from breast tissues that are either in lobules or in milk ducts. Ductal carcinoma is the common type of breast cancer starts from milk ducts and spread across the. Women between the...
Sentiment Analysis is the process of figuring out the emotions from a piece of writing that whether it is positive, negative or neutral and is used to tell the speaker's attitude. The trend, today, is to consider the opinions of a variety of individuals around the globe before purchasing an item using micro-blogging data. Customers tend to go over a lot of reviews about a particular item before buying...
An Artificial Neural Network (ANN) is a statistical data modeling tool inspired by the functionality and the structure of the biological nervous system. An ANN consists of processing elements known as neurons that are interconnected to each other and work in unison to answer a particular problem. Neural networks can be used in places where detecting trends and extracting patterns are too complex to...
There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, called sources are transformed to the feature presentation...
Depth of anesthesia is a matter of great importance in surgery. Determination of depth of anesthesia is a time consuming and difficult task carried out by experts. This study aims to decide a method that can classify EEG data automatically with a high accuracy and, so will help the experts for determination process. This study consists of three stages: feature extraction of EEG signals, feature selection,...
Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature,...
NeuroinformaticsNatural Language Processing (NeuroNLP) relies on clustering and classification for information categorization of biologically relevant extraction targets and for interconnections to knowledge-related patterns in event and text mined datasets. The accuracy of machine learning algorithms depended on quality of text-mined data while efficacy relied on the context of the choice of techniques...
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques,...
Panchromatic (PAN) satellite imagery comprises only a single band but it has finer resolution in comparison to the multi-spectral band imagery. In the case of feature extraction and classification, although the multi-spectral imagery has an advantage in availability of the different aspect of spectral properties of the ground coverage, the recent studies introducing intelligent classification and...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
The openness of the Web exposes opportunities for criminals to upload malicious content. In fact, despite extensive research, email based spam filtering techniques are unable to protect other web services. Therefore, a counter measure must be taken that generalizes across web services to protect the user from phishing host URLs. This paper describes an approach that classifies URLs automatically based...
User interaction with web sites generates a large amount of web access data stored in the web access logs. Those data can be used for e-commerce to conduct an evaluation of possessed website pages as one of the efforts to understand the desires of the user. Through classification techniques in web usage mining, we conducted an experiment to categorize a number of data obtained from the client log...
This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
Classification is a central problem in the fields of data mining and machine learning. Using a training set of labelled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more...
Multi-population genetic algorithms have been used with success for several multi-objective optimization problems. In this paper, we present a new general multi-population genetic algorithm for evolving decision trees. It was designed to improve the possibility of evolving balanced decision trees, simultaneously optimized for the predictions of each class. Single-population genetic algorithms namely...
Dropout rates for students in correspondence and open courses are on increase. There is a need of analysis of factors causing increase in dropout rate. The discovery of hidden knowledge from the educational data system by the effective process of data mining technology to analyze factors affecting student drop out can lead to a better academic planning and management to reduce students drop out from...
As increase in the internet services and usage with open access to sensitive data, necessity of security to these systems had become a need of the hour. Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more crucial issue. To detect the attacks hitting the network it is very obligatory to properly monitor the flow...
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