Fuzzy Decision Tree Research Articles

Resident activity recognition based on binary infrared sensors and soft computing

The basic concept of a smart space (SS) is to be aware of the context information related to environmental and human behavioral changes, and to provide appropriate services accordingly. To obtain context information, we may use video cameras, microphones, and other monitoring devices. Although these devices can obtain complex environmental data, they are not suitable for building private smart space (PSS) because of the privacy issue. Human users do not like being monitored in their private spaces. In this study, we investigate the possibility of recognizing certain activities using binary data collected by using infrared sensors. Infrared sensors have been used mainly for detecting the existence/absence of the residents in a region of interest. Here, we consider four types of activities, namely, No-Activity, Very-Weak-Activity, Weak-Activity, and Strong-Activity. Our main goal is to provide a way for building PSS using low-cost and non-privacy-sensitive devices. We have conducted some primary experiments by collecting user activity information using binary infrared sensors. Generally speaking, activity related sensor data are sensitive to various factors. To effectively address this issue, we propose a recognition method based on fuzzy decision tree. The results of the primary experiments show that the recognition rate of proposed method can be as high as 85.49%. The results are encouraging, and show the possibility of building PSS using binary infrared sensors.

A tool to design fuzzy decision trees for sustainability assessment

Assessment of sustainability is a multicriteria problem that addresses several criteria belonging to various themes structured in sustainability dimensions. There is the need for a relevant aggregation method in addition to the disaggregated indicators. To face this challenge, we developed a new aggregation method, CONTRA, which is based on a decision tree using fuzzy sets. We attempted to combine the advantages of previous tools like DEXi and FisPro that ensure simplicity, flexibility and transparency while limiting subjectivity in the design of decision trees. The results of two examples of implementation in the agricultural sector and a sensitivity test highlight the functionalities of the tool and its discrimination potential. The possibility offered by the tool to correct predetermined decision rules makes it possible to cope with compensation between input variables, an important issue in aggregation. The next step will be the finalisation of the Excel prototype in a user-friendly software.

Workplace assessment by fuzzy decision tree and TOPSIS methodologies to manage the occupational safety and health performance

Workplace assessment by fuzzy decision tree and TOPSIS methodologies to manage the occupational safety and health performance

An Enhanced Priority Scheduling Algorithm for Multi-Server Retrieval Cloud System

Cloud computing has become a most used business jargon in the area of high-performance computing as it provides on-demand services over ashared pool of internet resources. Process scheduling is one of the most important research issues to be focused on improving the performance of a cloud-based queuing system. The process of scheduling is to assign an appropriate resource to the task to achieve one or more objectives. Nowadays, the scheduling of jobs in cloud leads to NP-hard problem due to its large solution space and longer time to find an optimal solution. To find an optimal solution for the above issues and to improve theperformance of execution, a new priority based scheduling algorithm Enhanced Priority Scheduling Algorithm (EPSA) is proposed. The EPSA algorithm prioritizes jobs based on the job attribute. The attribute of jobsis execution time, resource requirement, CPU utilization, and dependency and target time. These parametersare combined and formulated for priority calculation. The job priority measure is used to generate a fuzzy decision tree (FDT). The FDT predicts the ordering position and service acquiring time for jobs that enter the cloud-based queuing system. EPSA algorithm improves the execution time and shows better performance compared to existing scheduling algorithms. The Proposed algorithm is implemented using JSIM-graph of JMT for performance modeling and evaluation.

Ensemble of Fuzzy Decision Tree for Efficient Indoor Space Recognition

In this paper, we expand the process of classification to an ensemble of fuzzy decision tree. For indoor space recognition, many research use Boosted Tree, consists of Adaboost and decision tree. The Boosted Tree extracts an optimal decision tree in stages. On each stage, Boosted Tree extracts the good decision tree by minimizing the weighted error of classification. This decision tree performs a hard decision. In most case, hard decision offer some error when they classify nearby a dividing point. Therefore, We suggest an ensemble of fuzzy decision tree, which offer some flexibility to the Boosted Tree algorithm as well as a high performance. In experimental results, we evaluate that the accuracy of suggested methods improved about 13% than the traditional one.

STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION

To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration.

Posterior probability based ensemble strategy using optimizing decision directed acyclic graph for multi-class classification

Ensemble strategy is important to develop a decomposition and ensemble method for multi-class classification problems. Most existing ensemble strategies use predetermined and heuristic decision rules. In this work, we build up the decision rules by optimizing decision directed acyclic graph (ODDAG) with classical and fuzzy decision trees to ensemble the posterior probabilities of binary classifiers from one-vs-one (OVO) or one-vs-all (OVA) decomposition strategies for multi-class classification problems. Four widely used extensible algorithms and ten decomposition and ensemble methods incorporating four binary classifiers (BCs) have been tested on 25 data sets. The empirical results show that the methods based on ensemble strategies using ODDAG are among the top methods that achieve the best performance in terms of two different measures.

Anterior osteoporosis classification in cervical vertebrae using fuzzy decision tree

Anterior Osteoporosis (AOs) in the cervical vertebrae is an osteoporosis complication and a common condition of vertebral irregularity caused by a decrease in bone density and strength, which can lead to fragile bone and fractures. Consequently, it is crucial to detect the AOs irregularity early so that appropriate pharmacological intervention can be done to reduce further complications. To do so via a computer approach, an efficient method that can provide high classification rate is required. Basing on the fuzzy logic theory, this article affords a new method for AOs (classes and severity) classification of the cervical radiography by designing a fuzzy decision tree (FDT) model. The method involves two main processed, namely i) segmentation process which employs the active shape model (ASM) based on the 9-anatomical points representation (9-APR) to segment the cervical vertebra shape boundary (C-VSB) and ii) fuzzy based feature extraction and classifier development, known as FDT method. The fuzzy set along with its membership functions are derived from the resulting C-VSB segment. It operates by extracting a specific angle descriptor (horizontal, vertical, and corner) as crisp input to the fuzzification inter-face to produce reasonable key indexing to the fuzzy interface system. Then, the defuzzification interface converts it into a crisp output that adequately represents the degree of AOs class and severity as appraisal values. The resulting fuzzy then acts as input to a basic concept of if-then rules called FDT to recognise and distinguish between vertebrae presented with/without AOs. Receiver operating characteristic (ROC) and area under curve (AUC) index evaluation methods are examined to offer quantitative evaluation between the medical ground truth versus FDT classifier predicted results. Results obtained on a set of 400 cervical vertebrae images indicate superb classification rate (R > 90 %) which suggest that the proposed FDT as an appropriate solution to AOs classification process for reliable vertebral fracture diagnosis. In summary, the findings confirmed the effectiveness of FDT as an excellent classifier to recognize and differentiate AOs classes and severity thus, able to provide important basis for pathology.

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants (NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neuro-fuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph (DUCG) has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm (GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

On the estimation of optimal number of clusters for the induction of fuzzy decision trees

Fuzzy decision tree (FDT) induction is a powerful methodology to extract human interpretable fuzzy classification rules. As far as our knowledge goes there is no recent comparative study of fuzzy cluster validity indices with an objective of using it for estimating the optimal number of clusters for each of the continuous attributes during the process of induction of FDT. In this paper, we study the performance of the FDT with optimal number of partitions for each node appearing in the FDT. By obtaining optimal number of fuzzy clusters, we capture the intrinsic structure of the attribute values during the formation of fuzzy partitions, which in turn improves the classification accuracy of FDT. Extensive computational experiments are conducted on FDT developed using Fuzzy ID3 and eight fuzzy cluster validity indices over 30 publicly available pattern classification datasets. Non-parametric statistical tests are conducted to test the null hypothesis.

On the estimation of optimal number of clusters for the induction of fuzzy decision trees

Fuzzy decision tree (FDT) induction is a powerful methodology to extract human interpretable fuzzy classification rules. As far as our knowledge goes there is no recent comparative study of fuzzy cluster validity indices with an objective of using it for estimating the optimal number of clusters for each of the continuous attributes during the process of induction of FDT. In this paper, we study the performance of the FDT with optimal number of partitions for each node appearing in the FDT. By obtaining optimal number of fuzzy clusters, we capture the intrinsic structure of the attribute values during the formation of fuzzy partitions, which in turn improves the classification accuracy of FDT. Extensive computational experiments are conducted on FDT developed using Fuzzy ID3 and eight fuzzy cluster validity indices over 30 publicly available pattern classification datasets. Non-parametric statistical tests are conducted to test the null hypothesis.

Fuzzy Intelligent System for Patients with Preeclampsia in Wearable Devices

Preeclampsia affects from 5% to 14% of all pregnant women and is responsible for about 14% of maternal deaths per year in the world. This paper is focused on the use of a decision analysis tool for the early detection of preeclampsia in women at risk. This tool applies a fuzzy linguistic approach implemented in a wearable device. In order to develop this tool, a real dataset containing data of pregnant women with high risk of preeclampsia from a health center has been analyzed, and a fuzzy linguistic methodology with two main phases is used. Firstly, linguistic transformation is applied to the dataset to increase the interpretability and flexibility in the analysis of preeclampsia. Secondly, knowledge extraction is done by means of inferring rules using decision trees to classify the dataset. The obtained linguistic rules provide understandable monitoring of preeclampsia based on wearable applications and devices. Furthermore, this paper not only introduces the proposed methodology, but also presents a wearable application prototype which applies the rules inferred from the fuzzy decision tree to detect preeclampsia in women at risk. The proposed methodology and the developed wearable application can be easily adapted to other contexts such as diabetes or hypertension.

Construction of a Reliability Structure Function Based on Uncertain Data

Structure function is one of the possible mathematical models of the real systems under study in reliability engineering. The structure function represents the correlation the system performance level and components states. The system performance level is defined from the states of all its components. It means that all possible components states and performance levels must be indicated and reflected in the structure function. Initial data for the analysis of real system is uncertain. New methods to construct a structure function based on initial uncertain data is proposed. Fuzzy decision trees (FDTs) are used in this method to transform initial uncertain data about a real system into an exact-defined a system structure function. The proposed method includes three principal steps for the structure function construction: 1) collection of data in the repository; 2) representation of the system model in the form of an FDT; and 3) construction of the structure function based on the FDT.

Alert System for Controlling Cyberbullying Words Using Fuzzy Logic and Fuzzy Inference Engine

Cyberbullying is a socially aggressive and has powerful negative effects for individuals, specifically adolescents and youngsters. Cyberbullying allows the offender to mask his or her identity behind a computer. This anonymity makes it easier for the offender to strike blows against a victim without having to see the victim’s physical response. The distancing effect that technological devices have on today’s youth often leads them to say and do crueler things compared to what is typical in a traditional face-to-face bullying situation. In the recent times many methods for automatic thoughts of mining in the online data are becoming increasingly important, to increase the safety parameter of the people. This framework is proposed to extract Cyberbully polarity from the Forum using Fuzzy logic technique. At first, the given input is pre-processed and the useful content is gathered. Subsequently, the pre -processed data will be sent to the features extraction method. Probabilities of the words are calculated by using Fuzzy Decision Tree Method. Fuzzy rules can be applied in all these features to extract the certain set of cyberbully words like bad words, insulting words,threatening words and terrorism words from the given input, hence we use text mining here. Finally this method will return the reduced and accurate cyberbully words. This method is performed by human annotation using the existing methods like Mamdani Fuzzy System and Naive Bayes classifier. Extensive experiments are performed by using fuzzy logic on crime debate forum and the results show that this proposed approach is better than the traditional one. Aggressive text detection in social networks allows identifying offenses and misbehavior, and leverages tasks such as cyberbullying detection. Social media became a very useful platform to express ourselves. The expressions have adverse reactions as well. We intend to take data from these platforms and make use of it to improve on the safety parameter. For the development of the system we take the data available on Twitter and filter all the useful contents.

An Improved Second Order Training Algorithm for Improving the Accuracy of Fuzzy Decision Trees

Fuzzy decision tree (FDT) is a powerful top-down, hierarchical search methodology to extract human interpretable classification rules. The performance of FDT depends on initial fuzzy partitions and other parameters like alpha-cut and leaf selection threshold. These parameters are decided either heuristically or by trial-and-error. For given set of parameters, FDT is constructed using any standard induction algorithms like Fuzzy ID3. Due to the greedy nature of induction process, there is a chance of FDT resulting in poor classification accuracy. To further improve the accuracy of FDT, in this paper, the authors propose the strategy called Improved Second Order- Neuro- Fuzzy Decision Tree (ISO-N-FDT). ISO-N-FDT tunes parameters of FDT from leaf node to roof node starting from left side of tree to its right and attains better improvement in accuracy with less number of iterations exhibiting fast convergence and powerful search ability.

Automatic image annotation using fuzzy association rules and decision tree

The problem of sharp boundary widely exists in image classification algorithms that use traditional association rules. This problem makes classification more difficult and inaccurate. On the other hand, massive image data will produce a lot of redundant association rules, which greatly decrease the accuracy and efficiency of image classification. To relieve the influence of these two problems, this paper proposes a novel approach integrating fuzzy association rules and decision tree to accomplish the task of automatic image annotation. According to the original features with membership functions, the approach first obtains fuzzy feature vectors, which can describe the ambiguity and vagueness of images. Then fuzzy association rules are generated from fuzzy feature vectors with fuzzy support and fuzzy confidence. Fuzzy association rules can capture correlations between low-level visual features and high-level semantic concepts of images. Finally, to tackle the large size of fuzzy association rules base, we adopt decision tree to reduce the unnecessary rules. As a result, the algorithm complexity is decreased to a large extent. We conduct the experiments on two baseline datasets, i.e. Corel5k and IAPR-TC12. The evaluation measures include precision, recall, F-measure and rule number. The experimental results show that our approach performs better than many state-of-the-art automatic image annotation approaches.

A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach

Knowledge inference systems are built to identify hidden and logical patterns in huge data. Decision trees play a vital role in knowledge discovery but crisp decision tree algorithms have a problem with sharp decision boundaries which may not be implicated to all knowledge inference systems. A fuzzy decision tree algorithm overcomes this drawback. Fuzzy decision trees are implemented through fuzzification of the decision boundaries without disturbing the attribute values. Data reduction also plays a crucial role in many classification problems. In this research article, it presents an approach using principal component analysis and modified Gini index based fuzzy SLIQ decision tree algorithm. The PCA is used for dimensionality reduction, and modified Gini index fuzzy SLIQ decision tree algorithm to construct decision rules. Finally, through PID data set, the method is validated in the simulation experiment in MATLAB.

SLMBC: spiral life cycle model-based Bayesian classification technique for efficient software fault prediction and classification

Software fault prediction and classification plays a vital role in the software development process for assuring high quality and reliability of the software product. Earlier prediction of the fault-prone software modules enables timely correction of the faults and delivery of reliable product. Generally, the fuzzy logic, decision tree and neural networks are deployed for fault prediction. But these techniques suffer due to low accuracy and inconsistency. To overcome these issues, this paper proposes a spiral life cycle model-based Bayesian classification technique for efficient software fault prediction and classification. In this process, initially the dependent and independent software modules are identified. The spiral life cycle model is used for testing the software modules in each life cycle of the software development process. Bayesian classification is applied to classify the software modules as faulty module and non-faulty module, by using the probability distribution models. Robust similarity-aware clustering algorithm performs clustering of the faulty and non-faulty software modules based on the similarity measure of the features in the dataset. From the experimental results, it is observed that the proposed method enables accurate prediction and classification of the faulty modules. The proposed technique achieves higher accuracy, precision, recall, probability of detection, F-measure and lower error rate than the existing techniques. The misclassification rate of the proposed technique is found to be lower than the existing techniques. Hence, the reliability of the software development process can be improved.

On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees

Abstract Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables.

Effect of Numerous Data Sets on Performance Prediction

There are many factors, which may affects performance. But the number of factors also affects on result of computational model. We are presenting a computational model to forecast students’ performance. To calculate we will use 8 different factors that are directly or indirectly influence performance. Influencing factors how much correlated to each other we also present this. Through this paper we classify those factors using fuzzy decision tree.