Iris Flower Research Articles

HOW MANY PARACHUTISTS WILL BE NECESSARY TO FIND A NEEDLE IN A PASTORAL - WHO IS A LUCKY ONE?

This article is a consideration on computer network intrusion detection using artificial neural networks, or whatever else using machine learning techniques. We assume an intrusion to a network is like a needle in a haystack not like a family of iris flower, and we consider how an attack can be detected by an intelligent way, if any.

Fuzzy Decision Tree

Decision trees have been widely used in machine learning. However, due to some reasons, data collecting in real world contains a fuzzy and uncertain form. The decision tree should be able to handle such fuzzy data. This paper presents a method to construct fuzzy decision tree. It proposes a fuzzy decision tree induction method in iris flower data set, obtaining the entropy from the distance between an average value and a particular value. It also presents an experiment result that shows the accuracy compared to former ID3.

METODE KLASIFIKASI BERSTRUKTUR POHON DENGAN ALGORITMA CRUISE

Two univariate split methods are proposed for the construction of classification trees with multyway splits named CRUISE (Classification Rule with Unbiased Interaction Selection and Estimation). A major strenght of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offer and when they differ in number of missing values. This is an advantage because inference from the tree sructures can be adversely affected by selection bias. These methods also improve interpretability of trees by reducing tree depht. Application of CRUISE algorithms to Fisher’s Iris data is to predict the variety of an Iris flower based on its petal and sepal lenght and widht. Results show that it only takes one variable to do so. Therefoce, the new methods are highly competitive in terms of computational speed and classification accuracy of future observation.

Balanced the Trade-offs Problem of ANFIS using Particle Swarm Optimization

Improving the approximation accuracy and interpretability of fuzzy systems is an important issue either in fuzzy systems theory or in its applications . It is known that simultaneous optimization both issues was the trade-offs problem, but it will improve performance of the system and avoid overtraining of data. Particle swarm optimization (PSO) is part of evolutionary algorithm that is good candidate algorithms to solve multiple optimal solution and better global search space. This paper introduces an integration of PSO dan ANFIS for optimise its learning especially for tuning membership function parameters and finding the optimal rule for better classification. The proposed method has been tested on four standard dataset from UCI machine learning i.e. Iris Flower, Haberman’s Survival Data, Balloon and Thyroid dataset. The results have shown better classification using the proposed PSO-ANFIS and the time complexity has reduced accordingly .

Composing with Cross-modal Correspondences: Music and Odors in Concert

We report two experiments designed to investigate cross-modal correspondences between a range of seven olfactory stimuli and both the pitch and instrument class of sounds as well as the angularity of visually presented shapes. The results revealed that odors were preferentially matched to musical features: For example, the odors of candied orange and iris flower were matched to significantly higher pitches than the odors of musk and roasted coffee. Meanwhile, the odor of creme brulee was associated with a more rounded shape than the musk odor. Moreover, by simultaneously testing cross-modal correspondences between olfactory stimuli and matches in two other modalities, we were able to compare the ratings associated with each correspondence. Stimuli judged as happier, more pleasant, and sweeter tended to be associated to both higher pitch and a more rounded shape, whereas other ratings seemed to be more specifically correlated with the choice of either pitch or shape. Odors rated as more arousing tended to be associated with the angular shape, but not with a particular pitch; odors judged as brighter were associated with higher pitch and, to a lesser extent, rounder shapes. In a follow-up experiment, we investigated whether people could match specific pieces of music (composed to represent odors) to three of the odors (candied orange, creme brulee, and ginger cookies). In one case (candied orange), a majority of the participants matched the odor to the intended piece of music. In another case (ginger cookies), another piece of music (than the one intended) was preferred. Finally, in the third case (creme brulee), people showed no preference in matching the odor to the pieces of music. Both theoretical and practical implications of these results are discussed.

Artificial neural network training using a new efficient optimization algorithm

Because search space in artificial neural networks (ANNs) is high dimensional and multimodal which is usually polluted by noises and missing data, the process of weight training is a complex continuous optimization problem. This paper deals with the application of a recently invented metaheuristic optimization algorithm, bird mating optimizer (BMO), for training feed-forward ANNs. BMO is a population-based search method which tries to imitate the mating ways of bird species for designing optimum searching techniques. In order to study the usefulness of the proposed algorithm, BMO is applied to weight training of ANNs for solving three real-world classification problems, namely, Iris flower, Wisconsin breast cancer, and Pima Indian diabetes. The performance of BMO is compared with those of the other classifiers. Simulation results indicate the superior capability of BMO to tackle the problem of ANN weight training. BMO is also applied to model fuel cell system which has been addressed as an open and demanding problem in electrical engineering. The promising results verify the potential of BMO algorithm.

A DIAMOND method of inducing classification rules for biological data

Identifying the classification rules for patients, based on a given dataset, is an important role in medical tasks. For example, the rules for estimating the likelihood of survival for patients undergoing breast cancer surgery are critical in treatment planning. Many well-known classification methods (as decision tree methods and hyper-plane methods) assume that classes can be separated by a linear function. However, these methods suffer when the boundaries between the classes are non-linear. This study presents a novel method, called DIAMOND, to induce classification rules from datasets containing non-linear interactions between the input data and the classes to be predicted. Given a set of objects with some classes, DIAMOND separates the objects into different cubes, and assigns each cube to a class. Via the unions of these cubes, DIAMOND uses mixed-integer programs to induce classification rules with better rates of accuracy, support and compact. This study uses three practical datasets (Iris flower, HSV patients, and breast cancer patients) to illustrate the advantages of DIAMOND over some current methods.

Yet another visualization of the Iris data: The bridge plot

The bridge plot is used to visualize the famous Iris data. With the help of trellis system to condition the data on species, the plot presents four iris flower traits in their original un its within a single panel. Such a plot can be employed when among four variables there are two groups each consisting of two variables having the same and comparable units.

Treatment with thidiazuron improves opening and vase life of iris flowers

The marketability of Dutch iris ( Iris × hollandica) cut flowers is limited by their short display life and frequent failure to open fully. We tested the ability of thidiazuron (TDZ), a phenyl-urea compound with cytokinin-like activity, to improve iris flower opening and longevity. A postharvest pulse with 0.2–1 mM TDZ for 6–24 h at 0 or 20 °C extended the vase life of flowers by up to 1.5 d relative to control (0 mM TDZ) stems in water. TDZ treatment also stimulated growth of the pedicel and ovary by up to 2.5 cm that, in turn, led to more complete opening of flowers. Inclusion of 1 mM gibberellic acid (GA 3) in the TDZ pulsing solution did not extend vase life further but did increase flower shoot elongation by an additional 1.0 cm. Provision of 20% sucrose with the TDZ plus GA 3 treatment had an additive effect, increasing vase life and shoot growth by a further 0.8 d and 1.0 cm, respectively. Pulsing stems with this combined treatment prior to storing flowers dry for 14 d at 0 °C provided maximum flower opening and display life after storage. Treatment with 0.5 mM TDZ stimulated a significant increase in ethylene production by flowers during their opening.

Fuzzy Inductive Learning Strategies

In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from “soft” instances. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.

Learning Concepts by Arranging Appropriate Training Order

Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result.

A fuzzy inductive learning strategy for modular rules

In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. The design of learning methods to learn concept descriptions in working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the maximum information gain is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy rules from “soft” instances, which differ from conventional instances in that they have class membership values. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.