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This paper seeks to implement and test a financial forecasting agent which employs time series, derived time series data, and news that are retrieved and extracted from the Web. This research focuses on the time series data of some individual stocks from the Indonesian Stock Exchange as well as the index data. The financial forecasting agent implemented is based on a Multilayer Neural Network trained...
The latest statistics of WHO show that approximately 500, 000 women die worldwide every year - the majority of them residing in developing countries - due to pregnancy related complications. The situation is so grave that UN has set a target of reducing Maternal Mortality Rate (MMR) by 75% till the year 2015 in its millennium development goals (MDGs). Therefore, the current focus of health care researchers...
Most Machine Learning systems target into inducing classifiers with optimal coverage and precision measures. Although this constitutes a good approach for prediction, it might not provide good results when the user is more interested in description. In this case, the induced models should present other properties such as novelty, interestingness and so forth. In this paper we present a research work...
In this paper, a new Hierarchical fuzzy classifier based on evolutionary boosting algorithms is proposed. The main goal of this paper is to improve the performance of fuzzy rule based classifiers through utilizing hierarchical structure for achieving fuzzy rules. The advantages of hierarchical fuzzy rules generated by evolutionary boosting algorithms are evaluated by comparison between the performance...
Tens of thousands of classifiers have been proposed so far. There is no best classifier among them. It is said that the performance of each classifier strongly depends on data sets used for comparison. In recent years, a number of data complexity measures have been proposed to characterize each data set. The aim of this study is to develop a framework for selecting an appropriate classifier and/or...
In this paper we tackle the problem of recognising movement classes in real-time surveillance video. We use a popular public dataset, the CAVIAR dataset, which contains ground truth labeling of people and their activities within a shopping centre environment. The task of movement classification is often performed using simple heuristic rules, and performance can suffer when an increased number of...
Aiming at the selection of knowledge meshes in the self-reconfiguration of knowledgeable manufacturing system, the method of fuzzy classification and searching for knowledge meshes based on information granular is proposed. Firstly, the matching degree, perfect degree and complexity coefficient between knowledge meshes are defined considering three aspects of quality, quantity and complexity. The...
Data uncertainty is common in real-world applications. Various reasons lead to data uncertainty, including imprecise measurements, network latency, outdated sources and sampling errors. These kinds of uncertainties have to be handled cautiously, or else the data mining results could be unreliable or wrong. In this demo, we will show uRule, a new rule-based classification and prediction system for...
The Knowledge File System (KFS) is a smart virtual file system that sits between the operating system and the file system. Its primary functionality is to automatically organize files in a transparent and seamless manner so as to facilitate easy retrieval. Think of the KFS as a personal assistant, who can file every one of you documents into multiple appropriate folders, so that when it comes time...
Recently, multivariate temporal data classification has been widely applied on many fields, such as bio-signals analysis, stocks prediction and weather forecasting. Multivariate temporal data contains hybrid type of attributes like numeric and categorical ones. However, most classification methods proposed in the past researches are not directly applicable to the multivariate temporal data with multiple...
We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (LSTWSVM) and term them as knowledge based TWSVM (KBTWSVM)/knowledge based LSTWSVM (KBLSTWSVM). Both of...
The presence of noise is common in any real data set and may adversely affect the accuracy, construction time and complexity of the classifiers. Models built by Fuzzy Rule Based Classification Systems are recognised for their interpretability, but traditionally these methods have not considered the presence of noise in the data, so it would be interesting to quantify its effect on them. The aim of...
Classification is a widely researched area in the machine learning and fuzzy communities with several approaches proposed by both communities. Some of the most relevant rule-based approaches from the machine learning community might include decision trees and rule inducers. The fuzzy community has also proposed many rule-based approaches, such as fuzzy decision trees and genetic fuzzy systems. This...
According to the traditional morphological classification divide the quality of traditional Chinese medicine White Peony Root into first grade second grade and the third grade. Discrete the chromatography data of the White Peony Root which obtained under the condition of standard test and also make the information reduction. Obtaining the great peaks of linear independent vectors and obtaining every...
This paper concentrates on studying the use of interval type-2 fuzzy sets for the pattern classification problem. Even though researchers recognize that type-2 fuzzy sets are more difficult to understand and use than type-1 fuzzy sets, the interest in the study is motivated by the additional power to represent uncertainty in different levels. The work developed here relies on the recent advances concerning...
This paper proposes a classification algorithm based on simplified fuzzy rules base combining fuzzy clustering with rough set. Firstly, generates fuzzy rules base using fuzzy clustering from numerical sample dates, and then simplifies the sample attributions using rough set theory, deletes the redundant rules, and gets the simplified fuzzy rules base, in order to make classification decision conveniently...
Ant Colony Optimization (ACO), an inspired algorithm from nature, has been successfully applied to classification tasks of data mining in recent years. This paper proposes a rule-based system for medical data mining by using a combination of ACO and fuzzy set theory, named FACO-Miner. FACO-Miner utilizes an ACO algorithm to learn a set of fuzzy rules from labeled data in parallel manner which causes...
The gait disturbances in Parkinson's disease (PD) patients occur occasionally and intermittently, appearing in a random, inexplicable manner. These disturbances include festinations, shuffling, and complete freezing of gait (FOG). Alternation of walking pattern decreases the quality of life and may result in falls. In order to recognize disturbances during walking in PD patients, we recorded gait...
When the training samples of well log data for Kohonen Self-Organizing Maps(KSOM) are large and high dimensional, the adjacent clusters may be overlap in a common region. In the paper, a new model of clustering analysis and recognition for well log data is proposed with Ultsch Emergent Self-organizing Maps(ESOM) of neural network. This method can overcome the weakness of KSOM and optimize the result...
This paper presents an extension to the Rule-Based Similarity (RBS) model a novel rough set approach to the problem of learning a similarity relation from data. The original model, proposed in [1], applied the notion of Tversky's feature contrast model in a rough set framework to facilitate an accurate case-based classification. In the dynamic RBS model, a dynamic reducts technique is used to broaden...
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