A note on no-free-lunch theorem

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.

Shortening of Proof Length is Elusive for Theorem Provers

There are many examples of failed strategies whose intention is to optimize a process but instead they produce worse results than no strategy at all. Many fall under the loose umbrella of the “no free lunch theorem”. In this paper we present an example in which a simple (but assumedly naive) strategy intended to shorten proof lengths in the propositional calculus produces results that are significantly worse than those achieved without any method to try to shorten proofs.This contrast with what was to be expected intuitively, namely no improvement in the length of the proofs. Another surprising result is how early the naive strategy failed. We set up a experiment in which we sample random classical propositional theorems and then feed them to two very popular automatic theorem provers (AProS and Prover9). We then compared the length of the proofs obtained under two methods: (1) the application of the theorem provers with no additional information; (2) the addition of new (redundant) axioms to the provers. The second method produced even longer proofs than the first one.

A predictive model for network intrusion detection using stacking approach

Due to the emerging technological advances, cyber-attacks continue to hamper information systems. The changing dimensionality of cyber threat landscape compel security experts to devise novel approaches to address the problem of network intrusion detection. Machine learning algorithms are extensively used to detect intrusions by dint of their remarkable predictive power. This work presents an ensemble approach for network intrusion detection using a concept called Stacking. As per the popular no free lunch theorem of machine learning, employing single classifier for a problem at hand may not be ideal to achieve generalization. Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance. A robust processing paradigm called Graphlab Create, capable of upholding massive data has been used to implement the proposed methodology. Two benchmark datasets like UNSW NB-15 and UGR’ 16 datasets are considered to demonstrate the validity of predictions. Empirical investigation has illustrated that the performance of the proposed approach has been reasonably good. The contribution of the proposed approach lies in its finesse to generate fewer misclassifications pertaining to various attack vectors considered in the study.

Artificial Intelligence Approach for Predicting TOC From Well Logs in Shale Reservoirs

Total organic carbon (TOC) is the most significant factor for shale oil and gas exploration and development which can be used to evaluate the hydrocarbon generation potential of source rock. However, estimating TOC is a challenge for the geological engineers because direct measurements of core analysis geochemical experiments are time-consuming and costly. Therefore, many AI technique has used for TOC content prediction in the shale reservoir where AI techniques have impacted positively. Having both strength and weakness, some of them can execute quickly and handle high dimensional data while others have limitation for handling the uncertainty, learning difficulties, and unable to deal with high or low dimensional datasets which reminds the “no free lunch” theorem where it has been proven that no technique or system be relevant to all issues in all circumstances. So, investigating the cutting-edge AI techniques is the contribution of this study as the resulting analysis gives top to bottom understanding of the different TOC content prediction strategies.

THE NO FREE LUNCH THEOREM: BAD NEWS FOR (WHITE'S ACCOUNT OF) THE PROBLEM OF INDUCTION

White (2015) proposes an a priori justification of the reliability of inductive prediction methods based on his thesis of induction-friendliness. It asserts that there are by far more induction-friendly event sequences than induction-unfriendly event sequences. In this paper I contrast White's thesis with the famous no free lunch (NFL) theorem. I explain two versions of this theorem, the strong NFL theorem applying to binary and the weak NFL theorem applying to real-valued predictions. I show that both versions refute the thesis of induction-friendliness. In the conclusion I argue that an a priori justification of the reliability of induction based on a uniform probability distribution over possible event sequences is impossible. In the outlook I consider two alternative approaches: (i) justification externalism and (ii) optimality justifications.