pdf/relief-based-feature-selection-introduction-and-review-47s24m8wih.pdf

Relief-based feature selection: Introduction and review.

Manias, Panics, and Crashes. A History of Financial Crises

Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm (FAST) is proposed and experimentally evaluated in this paper. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent, the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Extensive experiments are carried out to compare FAST and several representative feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with respect to four types of well-known classifiers, namely, the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature selection. The results, on 35 publicly available real-world high-dimensional image, microarray, and text data, demonstrate that the FAST not only produces smaller subsets of features but also improves the performances of the four types of classifiers.

A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data

Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

/pdf/efficient-prediction-of-cardiovascular-disease-using-machine-1g3efyu5h4.pdf

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Why are asset prices so much more volatile and so often detached from their fundamentals? Why does the burst of financial bubbles depress the real economy? This paper addresses these questions by constructing an infinite-horizon heterogeneous-agent general-equilibrium model with speculative bubbles. We show that agents are willing to invest in asset bubbles even though they have positive probability to burst. We prove that any storable goods, regardless of their intrinsic values, may give birth to bubbles with market prices far exceeding their fundamental values. We also show that perceived changes in the bubbles probability to bust can generate boom-bust cycles and produce asset price movements that are many times more volatile than the economy's fundamentals, as in the data.

Speculative Bubbles and Financial Crisis

Relief algorithms are successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation. In this paper, we propose a variant of ReliefF algorithm: ReliefMSS. We analyse the ReliefMSS parameters and compare ReliefF and ReliefMSS performances as regards the number of iterations, the number of random attributes, the noise effect, the number of nearest neighbours and the number of examples presented. We find that for the most of these parameters, ReliefMSS is better than ReliefF.

ReliefMSS: a variation on a feature ranking ReliefF algorithm

Stock market is a complex system composed from heterogeneous traders with highly non-linear interactions from which emerge a phenomenon of speculative bubble. To understand the role of heterogeneous behaviors of traders and interactions between them in the emergence of bubbles, we propose an agent-based model of double auction market, with asynchronous time management, where traders act asynchronously and take different times to make decisions. The market is populated by heterogeneous traders. In addition to fundamentalist, noise, and technical (chartist) traders, we propose a hybrid trader, which can switch between technical (chartist) and fundamentalist strategies integrating panicking behavior. We find that when market is populated by a majority of hybrid traders, we observe quite realistic bubble formation characterized by a boom phase when hybrid traders switch to technical behavior, followed by a relatively shorter burst phase when hybrid traders return to fundamentalist strategy and change to panicked state. The aim is to design agents which act asynchronously, with simple behaviors, but complex enough to produce realistic price dynamics, which provide a basis for developing agents with sophisticated decision-making processes.

/pdf/an-asynchronous-double-auction-market-to-study-the-formation-3nr68punzx.pdf

An Asynchronous Double Auction Market to Study the Formation of Financial Bubbles and Crashes

Agent-based artificial stock markets attracted much attention over the last years, and many models have been proposed. However, among them, few models take into account the social interactions and mimicking behaviour of traders, while the economic literature describes investors on financial markets as influenced by decisions of their peers and explains that this mimicking behaviour has a decisive impact on price dynamics and market stability. In this paper we propose a continuous double auction model of financial market, populated by heterogeneous traders who interact through a social network of influence. Traders use different investment strategies, namely: fundamentalists who make a decisions based on the fundamental value of assets; hybrids who are initially fundamentalists, but switch to a speculative strategy when they detect an uptrend in prices; noise traders who don’t have sufficient information to take rational decisions, and finally mimetic traders who imitate the decisions of their mentors on the interactions network. An experimental design is performed to show the feasibility and utility of the proposed model.

https://hal.archives-ouvertes.fr/hal-01782550/document

An Artificial Stock Market with Interaction Network and Mimetic Agents

Artificial stock markets are models design to capture essential properties of real stock markets in order to reproduce, analyze or understand market dynamics with computational experiments. Since there is a large variety of real stock markets and trading strategies, a large number of different artificial sock markets have been proposed. The researchers need constantly to test new structures of markets and new strategies of investment. In this paper we use Gaia methodology to provide a semi-formal specification of a generic model for artificial stock markets. The model is flexible in the sense that it can be used to develop several market structures and trading strategies, it can also adapted to support additional aspects. Keywords: Artificial stock market; multi-agents simulation; Agent-based computaional economic; agent oriented methodology; GAIA; AUML. 1. Introduction Despite the large amount of research in finance and economics, the behavior of financial markets is still poorly understood, and there is much controversy about the market mechanisms that lead to the observed price developments of the assets. Why would someone like to understand market dynamics? Investors and financial analysts, would like to understand them in order to make valuable investments. Market regulators need to understand them in order to improve market quality while scientists aim to test and state financial hypotheses [1]. Artificial stock markets are models of real stock markets, designed with the aim to study market dynamics. In agent-based artificial stock markets software agents are used to represent traders on a market. The idea to use intelligent software agents for studying market dynamics originates from the agent-based computational economics approach, the computational study of economies modeled as ev olving systems of autonomous interacting agents [2]. Since there is a large variety of real stock markets and trading strategies, a large number of different artificial sock markets have been proposed. Consequently, a number of researchers have proposed frameworks which can be configured to support multiple artificial stock markets (see [3 ,4,5]). However, the researcher s need constantly to test new structures of markets and new strategies of investment which are not taken into account by these frameworks. In this paper we use Gaia methodology to provide a semi-formal specification of a generic model for artificial stock markets. We specify a basic structure of market and a generic behavior of agents, and we give choice for developer to realize any implementation deemed appropriate. Through proper configuration, multiple market

International Conference on Computational Science, ICCS 2010 A semi-formal specification for a generic model of artificial stock markets

Artificial stock markets are models design to capture essential properties of real stock markets in order to reproduce, analy ze or understand market dynamics with computational experiments. Since there is a large variety of real stock markets and trading strategies, a large number of different artificial sock markets have been proposed. The researchers need constantly to test new structures of markets and new strategies of investment. In this paper we use Gaia methodology to provide a semi-formal specification of a generic model for artificial stock markets. The model is flexible in the sense that it can be used to develop several market structures and trading strategies, it can also adapted to support additional aspects.

A semi-formal specification for a generic model of artificial stock markets

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