To the programming community the algorithms described in this chapter and their methods are known as feature selection algorithms. This theoretical subject has been examined by researchers for decades and a large number of methods have been proposed. The terms attribute and feature are interchangeable and refer to the predictor values throughout this chapter, and for the remainder of the thesis. In this theoretical way of thinking, dimensionality reduction techniques are typically made up of two basic components, [21], [3], [9].
Evaluation Criterion, a measure to assess the relevance of an attribute subset
Search strategy, a procedure to generate candidate subsets for reduction.
When an evaluation criterion is decided upon, feature selection becomes a combinatorial search problem. Many iterations identify possible subsets of attributes that can be used to correctly classify the data and reduce the dimensionality of the sample space. Search strategies are generally divided into three categories, [3], [21]:
Complete, (exhaustive, best first, branch and bound, beam, etc.)
Heuristic, (forward/backward sequential, greedy, etc.)
Stochastic, (simulated annealing, genetic algorithm, etc.)
The feature weighting method as described by Wettschereck [20] is different from feature selection in that it evaluates the attributes individually and assigns them weights based on a bias, rather than comparing them to a user-defined threshold which determines its relevance. Each of the algorithms explored in this chapter uses feature weighting as a means to update the importance of a particular attribute, referring to its impact on the classification problem.
2.1.1 Related Works
A theoretical framework presented by Koller and Sahami introd...
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...e predictor is ordered, that is when there is a logical order of the values associated with the attribute, we must split the node in a way that preserves the existing order of values. For m distinct values this would give m – 1 possible splits. For example, if age is the predictor and the available values are 18 to 21 then there are four distinct ordered values. Thus, m = 4 and there are 3 possible splits that maintain order. They are, 18 versus 19-21, 18-19 versus 20-21, and 18-20 versus 21, [1].
Categorical predictors do not have a requirement pertaining to order. This gives a categorical variable with k categories (2¬k-1 – 1) possible splits, making the computational burden much heavier. There are also no restrictions on how a categorical predictor is split, but the theoretical workings of a categorical predictor are peripheral to the content within this thesis.
indicates towards a fraud. On eof the most important qualities or benefits of this model is that it understands the pattern in the data and generates the result. Once the result is generated the model checks as to how close was the result from the actual results. Based on this analysis the model adjusts its weights to give an accurate result the next time. Once this model has been trained to give accurate results, it can be used to analyze other data as well. Even when Neural Networks are widely accepted, they are not really used that much in the marketing industry merely by the fact that data preparation for this model is very complex time consuming as compared to the Regression Analysis. The marketers are much comfortable using the Regression Analysis over Neural Networks because of the ease of interpreting the results in the Regression Analysis.
Principal Component Analysis (PCA) is a multivariate analysis performed in purpose of reducing the dimensionality of a multivariate data set in order to recognize the shape or pattern of that data set. In other words, PCA is a powerful technique for pattern recognition that attempts to explain the variance of a large set of inter-correlated variables. It indicates the association between variables, thus, reducing the dimensionality of the data set. (Helena et al, 2000; Wunderlin et al, 2001; Singh et al, 2004)
A. SUNG, “Ranking importance of input parameters of neural networks,” Expert Systems with Applications, vol. 15, no. 3--4, pp. 405 – 411, 1998.
According to Gundecha and Liu (2012), the major aims of a data mining process include manipulating large-scale data and deciphering actionable patterns in them.
La optimización, también denominada programación matemática, sirve para encontrar la respuesta que proporciona el mejor resultado, la que logra mayores ganancias, mayor producción o felicidad o la que logra el menor costo, desperdicio o malestar. Con frecuencia, estos problemas implican utilizar de la manera más eficiente los recursos, tales como dinero, tiempo, maquinaria, personal, existencias, etc. Los problemas de optimización generalmente se clasifican en lineales y no lineales, según las relaciones del problema sean lineales con respecto a las variables. Existe una serie de paquetes de software para resolver problemas de optimización.
Due to the development of ICT, adaptive learning, which takes into account individual learners’ needs, is changing. Learners’ learning styles are one of the most significant characteristics. They can be categorized according to a number of criteria which are based on cognitive and emotional components of personality. Their combination leads to the countless individual variants of real learning methods which – to a certain degree – can be influenced by the current e-learning resources. When the e-learning resources can react to the learners’ input characteristics or their learning results, they become adaptive e-learning systems (AES) or intelligent AES.
In machine learning, Naive Bayesian Classification is a family of a simple probabilistic classifier based on the Bayes theorem (or Bayes’s rule) with Naive (Strong) independence assumption between the features. It is one of the most efficient and effective classification algorithms and represents a supervised learning method as well as a statistical method for classification. Naïve Bayesian classifiers assume that the effect of an attribute value on a given class is independent of the values of the other attributes. In other words Naïve Bayesian classifiers assume that there are no dependencies amongst attributes. This assumption is called class conditional independence. It is made to simplify the computations
This essay is going to define machine learning and describe some of the different areas within machine learning. It will summarise some of the algorithms used to achieve machine learning and describe some of the situations in which they can be applied, then compare these to human learning techniques and comment on there similarities and differences. It will then discuss Raymond Kurzweils singularity theories and its opposing views. Intro Machine learning is having a large impact on the way that computers can be used in many ways.
Each decision maker input his/her own judgment in the judgment matrix for each level of the decision tree. In the end, the final priorities of the alternatives are calculated. Those final priority matrices are aggregated and give the final group ranking of alternatives.
There are many different types of students. All students have their own way of studying and learning material. A student’s attitude is the most determining factor in how well a student performs academically. Some students are eager to learn and try their best; however, some students could care less about learning. Each year students decide whether they will succeed or fail in school. All students fall into one category or another. Students can be classified into three categories: Overachievers, Average Joes, and Do Not Give a Rips.
Sentiment analysis, also called as opinion mining, is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes and emotion towards entities such as products, services or organizations, individuals, issues, topics and their attributes. Sentiment analysis and opinion mining mainly focuses on opinions which express or imply positive, negative or neutral sentiments. Due to the big diversity and size of social media there is a need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task.
[19] K. Burgers, et al., A Comparative Analysis of Dimension Reduction Algorithms on Hyperspectral Data, 2009
Classification: Classification in data mining is an important task used for assigning a data item to a predefined set of classes. It is described as a function that maps a data item into one of the several predefined classes [6]. There is multitude of forms in which data exists in software engineering. By Classification is used to build defect prediction model by assimilating the already processed defect data and the use it to predict defects in future version of software. It is aimed at determining if a software module has a higher risk of defect. Classification usually assesses the data from earlier project versions as well as from similar data of other projects to establish a classification model. This model will be used to forecast the software defects. Many classification algorithms are used in software engineering to solve variety of problems under different phases. Classifications are used to identify bug types and thus help to build bug detector. A decision tree is a critical tool in classification technique that helps to identify the risky modules in software based on attributes of system and modules. Even though classification and assignment can be automated, it is often done by humans, especially when a bug is wrongly registered by the reporter in the bug
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.
C. Akalya devi, K. E. Kannammal and B. Surendiran, A Hybrid Feature Selection Model For Software Fault Prediction, International Journal on Computational Sciences & Applications (IJCSA) Vol2, No.2, April 2012,