Iris Data Classification Problem Research Articles

Generating Weighted Fuzzy Rules from Training Data for Dealing with the Iris Data Classification Problem

The most important task in the design of fuzzy classification systems is to find a set of fuzzy rules from training data to deal with a specific classification problem. In this paper, we present a new method to generate weighted fuzzy rules from training data to deal with the Iris data classification problem. First, we convert the training data to fuzzy rules, and then we merge those fuzzy rules in order to reduce the number of fuzzy rules. Then, we calculate the weight of each input variable appearing in the generated fuzzy rules by the relationships of input variables. The proposed weighted fuzzy rules generation method gets a higher average classification accu- racy rate than the existing methods.

A New Approach for Handling the Iris Data Classification Problem

In this paper, we present a new method to deal with the Iris data classification problem based on the distribution of training instances. First, we find two useful attributes of the Iris data from the training instances that are more suitable to deal with the classification problem. It means that the distribution of the values of these two useful attributes of the three species (i.e., Setosa, Versicolor and Virginica) has less overlapping. Then, we calculate the average attribute values and the standard deviations of these two useful attributes. We also calculate the overlapping areas formed by the values of these two useful attributes between species of the training instances, the average attribute values, and the standard deviations of the values of these two useful attributes of each species. Then, we calculate the difference between the values of these two useful attributes of a testing instance to be classified and the values of these two useful attributes of each species of the training instances. We choose the species that has the smallest difference between the values of these two useful attributes of the testing instance and the values of these two useful attributes of each species of the training instances as the classification result of the testing instance. The proposed method gets a higher average classification accuracy rate than the existing methods.

Data Driven Fuzzy Modeling for Sugeno and Mamdani Type Fuzzy Model using Memetic Algorithm

The process of fuzzy modeling or fuzzy model identification is an arduous task. This paper presents the application of Memetic algorithms (MAs) for the identification of complete fuzzy model that includes membership function design for input and output variables and rulebase generation from the numerical data set. We have applied the algorithms on four bench mark data: A rapid Ni-Cd battery charger, the Box & Jenkinss gas-furnace data, the Iris data classification problem and the wine data classification problem. The comparison of obtained results from MAs with Genetic algorithms (GAs) brings out the remarkable efficiency of MAs. The result suggests that for these problems the proposed approach is better than those suggested in the literature.

Fuzzy Model Identification: A Firefly Optimization Approach

Nature-inspired methodologies are currently among the most powerful algorithms for optimization problems. This paper presents a recent nature-inspired algorithm named Firefly algorithm (FA) for automatically evolving a fuzzy model from numerical data. FA is a meta-heuristic inspired by the flashing behavior of fireflies. The rate and the rhythmic flash, and the amount of time form part of the signal system to attract other fireflies. The paper discusses fuzzy modeling for zero-order Takagi-Sugeno-Kang (TSK) type fuzzy systems. Simulations on two well known problems, one battery charger that is a fuzzy control problem and another Iris data classification problem are conducted to verify the performance of above approach. The results indicate that the FA is a very promising optimizing algorithm for evolving fuzzy logic based Systems as compared to some of the existing approaches. General Terms Soft Computing, Fuzzy Model Identification.

A general insight into the effect of neuron structure on classification

This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.

Generating fuzzy rules from training instances for fuzzy classification systems

In recent years, many methods have been proposed to generate fuzzy rules from training instances for handling the Iris data classification problem. In this paper, we present a new method to generate fuzzy rules from training instances for dealing with the Iris data classification problem based on the attribute threshold value @a, the classification threshold value @b and the level threshold value @c, where @a@?[0,1], @b@?[0,1] and @c@?[0,1]. The proposed method gets a higher average classification accuracy rate than the existing methods.

A new method to deal with fuzzy classification problems by tuning membership functions for fuzzy classification systems

This paper presents a new method to construct and tune membership functions and generate fuzzy classification rules from training instances for handling the Iris data classification problem. First, we find two attributes of the Iris data from the training instances that are suitable to serve as classification criteria. Then, we construct and tune the membership functions of these two attributes and generate fuzzy classification rules from the training instances. The proposed method generates the same number of fuzzy classification rules as the number of species of the training instances. It generates fewer fuzzy classification rules and can get a higher average classification accuracy rate than the existing methods.

A New Method for Handling Fuzzy Classification Problems Using Clustering Techniques

It is obvious that fuzzy classification systems are important applications of the fuzzy set theory. Fuzzy classification systems can deal with perceptual uncertainties in classification problems. In recent years, many methods have been proposed to deal with fuzzy classification problems. In this paper, we present a new method to deal with the Iris data classification problem based on the concept of fuzzy compatibility relations for finding the cluster centers of training instances. The proposed method can get a higher average classification accuracy rate to deal with the Iris data classification problem than the existing methods.

A NEW METHOD TO GENERATE FUZZY RULES FROM TRAINING INSTANCES FOR HANDLING CLASSIFICATION PROBLEMS

A major task in developing a fuzzy classification system is to generate a set of fuzzy rules from training instances to deal with a specific classification problem. In recent years, many methods have been developed to generate fuzzy rules from training instances. We present a new method to generate fuzzy rules from training instances to deal with the Iris data classification problem. The proposed method can discard some useless input attributes to improve the average classification accuracy rate. It can obtain a higher average classification accuracy rate and it generates fewer fuzzy rules and fewer input fuzzy sets in the generated fuzzy rules than the existing methods.

GA-fuzzy modeling and classification: complexity and performance

The use of genetic algorithms (GAs) and other evolutionary optimization methods to design fuzzy rules for systems modeling and data classification have received much attention in recent literature. Authors have focused on various aspects of these randomized techniques, and a whole scale of algorithms have been proposed. We comment on some recent work and describe a new and efficient two-step approach that leads to good results for function approximation, dynamic systems modeling and data classification problems. First, fuzzy clustering is applied to obtain a compact initial rule-based model. Then this model is optimized by a real-coded GA subjected to constraints that maintain the semantic properties of the rules. We consider four examples from the literature: a synthetic nonlinear dynamic systems model, the iris data classification problem, the wine data classification problem, and the dynamic modeling of a diesel engine turbocharger. The obtained results are compared to other recently proposed methods.

A new method for constructing membership functions and fuzzy rules from training examples

To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the alpha-cuts of equivalence relations and the alpha-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.

An efficient algorithm for automatic knowledge acquisition

This paper presents a new algorithm for extracting IF-THEN rules from examples. The algorithm employs an efficient rule searching method and a simple metric for assessing rule generality and accuracy. The paper illustrates step by step the operation of the algorithm and discusses its performance on the IRIS data classification problem.