An Empirical Overview of the No Free Lunch Theorem and Its Effect on Real-World Machine Learning Classification

2016 ◽  
Vol 28 (1) ◽  
pp. 216-228 ◽  
Author(s):  
David Gómez ◽  
Alfonso Rojas
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Optimization Problem ◽  
Data Sets ◽  
Free Lunch ◽  
Real World Data ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
No Free Lunch Theorem ◽  
No Free Lunch

A sizable amount of research has been done to improve the mechanisms for knowledge extraction such as machine learning classification or regression. Quite unintuitively, the no free lunch (NFL) theorem states that all optimization problem strategies perform equally well when averaged over all possible problems. This fact seems to clash with the effort put forth toward better algorithms. This letter explores empirically the effect of the NFL theorem on some popular machine learning classification techniques over real-world data sets.

A note on no-free-lunch theorem

2021 ◽  
Author(s):  
Lidong Wu
Keyword(s):  
Machine Learning ◽  
Theoretical Result ◽  
Data Driven ◽  
Np Hard ◽  
Free Lunch ◽  
No Free Lunch Theorem ◽  
Data Driven Approach ◽  
Hard Problems ◽  
No Free Lunch ◽  
Np Hard Problems

The No-Free-Lunch theorem is an interesting and important theoretical result in machine learning. Based on philosophy of No-Free-Lunch theorem, we discuss extensively on the limitation of a data-driven approach in solving NP-hard problems.

scholarly journals A Systematic Methodology on Class Imbalanced Problems involved in the Classification of Real-World Datasets

2019 ◽  
Vol 8 (3) ◽  
pp. 7071-7081
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Imbalanced Data ◽  
Past Research ◽  
Future Research ◽  
Data Sets ◽  
Time Data ◽  
Real World Data ◽  
The Past ◽  
Research Studies

Current generation real-world data sets processed through machine learning are imbalanced by nature. This imbalanced data enables the researchers with a challenging scenario in the context of perdition for both the machine learning and data mining algorithms. It is observed from the past research studies most of the imbalanced data sets consists of the major classes and minor classes and the major class leads the minor class. Several standards and hybrid prediction algorithms are proposed in various application domains but in most of the real-time data sets analyzed in the studies are imbalanced by nature thereby affecting the accuracy of the prediction. This paper presents a systematic survey of the past research studies to analyze intrinsic data characteristics and techniques utilized for handling class-imbalanced data. In addition, this study reveals the research gaps, trends and patterns in existing studies and discusses briefly on future research directions

Machine Learning for Structured Finance

2018 ◽  
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Free Lunch ◽  
Structured Finance ◽  
Trade Off ◽  
No Free Lunch Theorem ◽  
Potential Benefits ◽  
Bias Variance ◽  
No Free Lunch

Machine learning and artificial intelligence have evolved beyond simple hype and have integrated themselves in business and in popular conversation as an increasing number of smart applications profoundly transform the way we work and live. This article defines machine learning in terms of potential benefits and pitfalls for a nontechnical audience, and gives examples of popular and powerful machine learning algorithms: k-means clustering, principal component analysis, and artificial neural networks. Three important philosophical challenges of machine learning are introduced: the no free lunch theorem, the curse of dimensionality, and the bias–variance trade-off.

scholarly journals Collective Classification in Network Data

AI Magazine ◽  
2008 ◽  
Vol 29 (3) ◽  
pp. 93 ◽  
Author(s):  
Prithviraj Sen ◽  
Galileo Namata ◽  
Mustafa Bilgic ◽  
Lise Getoor ◽  
Brian Galligher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Communication Networks ◽  
Real World ◽  
Biological Networks ◽  
Real World Data ◽  
Machine Learning Classification ◽  
The Past ◽  
Inference Algorithms ◽  
Real World Applications ◽  
Communication Links

Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.

scholarly journals Hfinger: Malware HTTP Request Fingerprinting

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 507
Author(s):  
Piotr Białczak ◽  
Wojciech Mazurczyk
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Experimental Evaluation ◽  
Data Sets ◽  
Real World Data ◽  
Malicious Software ◽  
Default Mode ◽  
World Data ◽  
Effectiveness Analysis ◽  
Http Protocol

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

scholarly journals Different algorithms, different models

2021 ◽  
Author(s):  
Martyna Daria Swiatczak
Keyword(s):  
Comparative Analysis ◽  
Real World ◽  
Qualitative Comparative Analysis ◽  
Comparative Methods ◽  
Data Sets ◽  
Simulation Studies ◽  
Threshold Values ◽  
Real World Data ◽  
Software Packages ◽  
Methodological Approaches

AbstractThis study assesses the extent to which the two main Configurational Comparative Methods (CCMs), i.e. Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), produce different models. It further explains how this non-identity is due to the different algorithms upon which both methods are based, namely QCA’s Quine–McCluskey algorithm and the CNA algorithm. I offer an overview of the fundamental differences between QCA and CNA and demonstrate both underlying algorithms on three data sets of ascending proximity to real-world data. Subsequent simulation studies in scenarios of varying sample sizes and degrees of noise in the data show high overall ratios of non-identity between the QCA parsimonious solution and the CNA atomic solution for varying analytical choices, i.e. different consistency and coverage threshold values and ways to derive QCA’s parsimonious solution. Clarity on the contrasts between the two methods is supposed to enable scholars to make more informed decisions on their methodological approaches, enhance their understanding of what is happening behind the results generated by the software packages, and better navigate the interpretation of results. Clarity on the non-identity between the underlying algorithms and their consequences for the results is supposed to provide a basis for a methodological discussion about which method and which variants thereof are more successful in deriving which search target.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

scholarly journals Combining Outcome-Based and Preference-Based Matching: A Constrained Priority Mechanism

2021 ◽  
pp. 1-24
Author(s):  
Avidit Acharya ◽  
Kirk Bansak ◽  
Jens Hainmueller
Keyword(s):  
Machine Learning ◽  
Host Country ◽  
Real World ◽  
Test Scores ◽  
Standardized Test ◽  
Market Design ◽  
Standardized Test Scores ◽  
Real World Data ◽  
Refugee Families ◽  
Strategy Proof

Abstract We introduce a constrained priority mechanism that combines outcome-based matching from machine learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the probability of employment, whereas in the student assignment context, it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families and students) based on their preferences, but subject to meeting the planner’s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner’s threshold.

Improving recommender systems’ performance on cold-start users and controversial items by a new similarity model

2016 ◽  
Vol 12 (2) ◽  
pp. 126-149 ◽  
Author(s):  
Masoud Mansoury ◽  
Mehdi Shajari
Keyword(s):  
Real World ◽  
Design Methodology ◽  
Cold Start ◽  
Selection Function ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
User Similarity ◽  
Active User ◽  
Similarity Model

Purpose This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users). Design/methodology/approach A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results. Findings Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items. Originality/value In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

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